Communication method and communication apparatus

Abstract

The present application provides a communication method and a communication apparatus. The method comprises: a first network element determines a first entity and a first identity, and sends a first request message to the first entity, the first request message being used for requesting the first entity to execute a first federated learning task with the first identity, and the first identity being used for indicating the identity of the first entity in executing the first federated learning task. According to the method, the first identity is sent to the first entity, so that the first entity can effectively execute the first federated learning task with the first identity, thereby improving the execution efficiency of the first federated learning task.

Description

Communication method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on January 11, 2024, with application number 202410048080.1 and invention name “A Communication Method and Communication Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The embodiments of the present application relate to the field of communication technology, and more specifically, to a communication method and a communication device. Background Art

[0003] Federated learning (FL) allows multiple participants to exchange model parameters through secure mechanisms without exchanging training data, thereby achieving collaborative training. Federated learning includes horizontal federated learning and vertical federated learning. Horizontal federated learning, also known as feature-aligned federated learning, means that the data features of the participants are aligned. Horizontal federated learning can increase the total number of training samples. Vertical federated learning combines the different data features of common samples from multiple participants. Training data for each participant is vertically partitioned. Vertical federated learning, also known as sample-aligned federated learning, means that the training samples of the participants are aligned. Vertical federated learning can increase the feature dimensionality of training data. Federated learning can effectively help multiple institutions utilize data and conduct learning and modeling while meeting user privacy, data security, and government regulations.

[0004] However, current mobile network systems, such as the fifth-generation core (5GC) network, do not support the creation and execution of vertical federated learning tasks. Therefore, how to effectively execute vertical federated learning tasks is an issue that needs to be considered. Summary of the Invention

[0005] The present application provides a communication method and a communication device that can effectively perform vertical federated learning tasks.

[0006] In a first aspect, a communication method is provided. The method may be executed by a first network element (e.g., a task initiator), or may be executed by a chip, circuit, or logic module of the first network element, although this application does not limit this. For ease of description, the following description will be based on an example of execution by the first network element.

[0007] The method includes: a first network element determines a first entity and a first identity, where the first identity is used to indicate the identity of the first entity in performing a first federated learning task; the first network element sends a first request message to the first entity, where the first request message is used to request the first entity to perform the first federated learning task with the first identity.

[0008] The first network element supports initiating the first federated learning task, or in other words, the first network element has the ability to initiate the first federated learning task. For example, the first network element can be a coordinator, NWDAF, or AF.

[0009] Optionally, the first request message includes the first identity.

[0010] That is, the first entity is an entity that has the ability to perform the first federated learning task as a first identity.

[0011] Optionally, the first federated learning task can be replaced by other names such as: first federated learning, or first federated learning function, or first federated learning process (process), or first federated learning activity. The entity that supports the execution of the first federated learning task here can be understood as: an entity (or network element or device or node, etc.) that has the ability to execute the first federated learning task, or in other words, the entity supports the execution of the first federated learning task (or activity, or process, etc.).

[0012] For example, the first federated learning task may include one or more of the following: network performance analysis, artificial intelligence (AI) model training, facial recognition, or cross-institutional medical data analysis and disease prediction. Alternatively, the federated learning task in the embodiments of this application may refer to a vertical federated learning task, for which specific interpretation please refer to the relevant description above.

[0013] According to the above solution, after determining the first entity and the first identity, the first network element sends the first identity to the first entity, so that the first entity can effectively execute the first federated learning task with the first identity, thereby improving the execution efficiency of the first federated learning task.

[0014] In some implementations, the first identity includes a master participant or a slave participant, wherein the master participant supports providing labels in the first federated learning task, and the labels correspond to the first federated learning task; or, the master participant supports providing labels or labels and data in the first federated learning task; and the slave participant supports providing data in the first federated learning task.

[0015] That is, the first entity supports providing labels and / or data in performing the first federated learning task.

[0016] Among them, the label is used for the first federated learning task, for example, for training and / or evaluating the model corresponding to the first federated task; the data is used for the first federated learning task, for example, for analysis, model training and / or reasoning corresponding to the first federated task. The data may refer to the samples used by the first entity in performing the first federated learning task, such as the traffic data of the terminal on the AF, or the business data of the terminal on the AF, etc.

[0017] In some implementations, the method also includes: the first network element obtains at least one candidate entity and identity information of at least one candidate entity, where the identity information of at least one candidate entity is used to indicate the identity supported by at least one candidate entity in the first federated learning task; the first network element determines the first entity and the first identity, including: the first network element determines the first entity and the first identity from the at least one candidate entity and the identity information of at least one candidate entity.

[0018] Based on the above scheme, the first network element can select or determine the first entity and the first identity from the obtained at least one candidate entity and the identity information of at least one candidate entity, that is, find the first entity and the first identity suitable for executing the first federated learning task, so that the first entity can subsequently effectively execute the first federated learning task with the first identity, thereby improving the execution efficiency of federated learning.

[0019] In some implementations, the first network element obtains at least one candidate entity and identity information of at least one candidate entity, including: the first network element receives at least one second entity and identity information of at least one second entity from the second network element, the second network element supports discovering entities that perform the first federated learning task, or the second entity supports providing information of entities capable of performing the first federated learning task, wherein at least one second entity supports performing the first federated learning task, and at least one second entity includes at least one candidate entity.

[0020] In some implementations, the first network element receives at least one second entity and identity information of at least one second entity from the second network element, including: the first network element sends a third request message to the second network element, and the third request message is used to obtain an entity that supports executing the first federated learning task; the first network element receives a third response message from the second network element, and the third response message includes at least one candidate entity and identity information of at least one candidate entity.

[0021] Among them, the entity that supports the execution of the first federated learning task can be understood as: the entity has the ability to execute the first federated learning task.

[0022] Optionally, the third request message includes one or more of the following: multiple types, a first analysis identifier, a first group identifier, information of a vertical federal alliance, or a first interoperability identifier, etc., where the type refers to the type of the entity, such as AF type and / or NF type.

[0023] Based on the above scheme, the first network element can obtain at least one candidate entity and identity information of at least one candidate entity from the second network element (for example, the entity discovery function network element), wherein the at least one candidate entity can be an AF instance and / or NF instance determined by the second network element based on multiple types, or it can be an AF instance and / or NF instance determined by the second network element based on at least one of the first group identifier, the first analysis identifier, or the first interoperability identifier.

[0024] In some implementations, before the first network element determines the first entity and the first identity, the method also includes: the first network element sends a second request message to at least one candidate entity, the second request message is used to request the at least one candidate entity to prepare to perform the first federated learning task; the first network element receives a second response message from the at least one candidate entity, the second response message is used to indicate that the at least one candidate entity agrees to support the execution of the first federated learning task with the first identity.

[0025] Exemplarily, at least one candidate entity includes a first entity, which means that the first network element sends a second request message to the first entity, and the second request message is used to request the first entity to prepare to perform the first federated learning task; the first network element receives a second response message from the first entity, and the second response message is used to indicate that the first entity agrees or supports performing the first federated learning task with the first identity.

[0026] In some implementations, the second request message includes at least one identity supported by the first entity in the first federated learning task, the at least one identity supported by the first entity in the first federated learning task includes a first identity, and the at least one supported identity includes a master participant and / or a slave participant.

[0027] Optionally, the second request message includes a first identity, where the first identity refers to the identity that the first network element requests the first entity to support, or in other words, requests the first entity to perform subsequent first federated learning tasks with the first identity. The identity requested to be supported by the first entity can be a master participant and / or a slave participant.

[0028] In some implementations, the second response message includes the first identity.

[0029] Based on the above scheme, the first network element finally determines the participants who will execute the first federated learning task and their identity information only after determining that the first entity agrees to execute the first federated learning task with the first identity, thereby improving the execution efficiency of the first federated learning task.

[0030] In some implementations, the second request message further includes information of a third network element, where the third network element supports coordinating the first entity to perform the first federated learning task.

[0031] In some implementations, the method also includes: the first network element obtains information about the third network element and identity information of the third network element, and the identity information of the third network element is used to indicate that the third network element supports coordinating the first entity to perform the first federated learning task, or in other words, the identity information of the third network element is used to indicate that the third network element has the ability to coordinate the first entity to perform the first federated learning task.

[0032] In some implementations, the first network element obtains the information of the third network element and the identity information of the third network element, including: the first network element receives the information of the third network element and the identity information of the third network element from the second network element.

[0033] Optionally, the first network element may obtain the information of the third network element and the identity information of the third network element through signaling configuration or pre-configuration.

[0034] In some implementations, the method also includes: the first network element sends a fifth request message to the third network element, the fifth request message is used to request the third network element to coordinate the first entity to perform the first federated learning task; the first network element receives a fifth response message from the third network element, the fifth response message is used to indicate that the third network element agrees to coordinate the first entity to perform the first federated learning task.

[0035] Based on the above scheme, the first network element and the third network element interact with each other through information, and determine that the third network element coordinates the first entity to perform the first federated learning task as a coordinator, so as to improve the execution efficiency of subsequent first federated learning tasks.

[0036] In some implementations, before the first network element sends a first request message to the first entity, the method also includes: the first network element sends a sixth request message to the third network element, the sixth request message is used to request the creation or coordination of the first federated learning task, and the sixth request message includes the first entity and the first identity.

[0037] Based on the above scheme, the first network element sends the first entity and the first identity participating in the execution of the first federated learning task to the third network element, that is, clarifies the identity of each first entity in the execution of the first federated learning task, such as providing labels and / or data, so as to facilitate the third network element to coordinate or organize the first entity to participate in the execution of the first federated learning task with the first identity, thereby effectively improving the execution efficiency of federated learning.

[0038] In some implementations, before the first network element determines the first entity and the first identity, the method also includes: the first network element obtains a first analysis identifier, the first analysis identifier corresponds to a first federated learning task; the first network element determines to trigger the first federated learning task based on the first analysis identifier.

[0039] Based on the above solution, the first network element can determine to initiate the first federated learning task according to the first analysis identifier, and then determine the first entity that executes the first federated learning task, as well as the identity of the first entity in executing the first federated learning task.

[0040] Exemplarily, the first network element may obtain the first analysis identifier by: the first network element may receive a subscription request message from a consumer (e.g., an NWDAF including AnLF, or an NWDAF including MTLF), where the subscription request message includes the first analysis identifier, and the subscription request message is used to subscribe to federated learning model provisioning or training (e.g., a subscription request for ML model provisioning / training). The subscription request message may be a model subscription request message or an analysis subscription request message.

[0041] Exemplarily, the first analysis identifier can be predefined or preconfigured, where predefinition can include predefinition, such as protocol definition, and preconfiguration can be achieved by pre-saving corresponding codes, tables, strings or other methods for indicating the first analysis identifier in the first network element. This application does not limit its specific implementation method.

[0042] It should be understood that the first analysis identifier can be used to indicate a specific function or service associated with the model, that is, the model can be used to perform the specific function or service, or in other words, the model supports the execution of the specific function or service corresponding to the first analysis identifier. This specific function or service may be, for example, network performance analysis. The first analysis identifier corresponds to the first federated learning task, and it can be understood that the first analysis identifier is used to indicate the first federated learning task.

[0043] Optionally, the first network element may also obtain other information and, based on the correspondence between the other information and the first federated learning task, determine to trigger the first federated learning task. Exemplarily, the other information may include one or more of the following: a first group identifier, a first analysis identifier, a first interoperability identifier, a task identifier corresponding to the first federated learning task, or information about a vertical federated alliance. For specific explanations, please refer to the relevant description below.

[0044] In a second aspect, a communication method is provided. The method may be performed by a second network element (e.g., an entity discovery function network element), or may be performed by a chip, circuit, or logic module of the second network element, although this application does not limit this. For ease of description, the following description is based on an example of execution by the second network element.

[0045] The method includes: the second network element receives a third request message, the third request message is used to obtain an entity that supports executing the first federated learning task, and the second network element supports discovering the entity that executes the first federated learning task; the second network element determines at least one candidate entity and identity information of at least one candidate entity, and the identity information of at least one candidate entity is used to indicate the identity supported by at least one candidate entity in the first federated learning task; the second network element sends a third response message, and the third response message includes at least one candidate entity and identity information of at least one candidate entity.

[0046] In some implementations, the third request message includes multiple types, each of which corresponds to at least one candidate entity. The type here refers to the type of the entity, such as AF type and / or NF type.

[0047] In some implementations, the method further includes: the second network element sending information of the third network element and identity information of the third network element to the first network element, where the identity information of the third network element is used to indicate that the third network element supports coordinating the first entity to perform the first federated learning task.

[0048] In some implementations, the method also includes: the second network element receives a registration request message from at least one second entity, the registration request message includes identity information of the at least one second entity, wherein the second entity supports the execution of the first federated learning task, and it should be understood that the at least one second entity includes at least one candidate entity, and the at least one candidate entity includes the first entity.

[0049] In some implementations, the second network element receives a third request message, including: the second network element receives the third request message from the first network element, and the first network element supports initiating a first federated learning task; or, the second network element receives the third request message from a third network element, and the third network element supports coordinating the first entity to perform the first federated learning task, and at least one candidate entity includes the first entity.

[0050] The beneficial effects of the above-mentioned second aspect and certain implementation methods can be referred to the corresponding description of the first aspect, and will not be repeated here.

[0051] In a third aspect, a communication method is provided. This method may be performed by a third network element (e.g., a task coordinator), or may be performed by a chip, circuit, or logic module of the third network element, although this application does not limit this. For ease of description, the following description will be based on an example of execution by a third network element.

[0052] The method includes: a third network element obtains a first entity and a first identity, the first identity is used to indicate the identity of the first entity in executing a first federated learning task, and the third network element supports coordinating the first entity to execute the first federated learning task; the third network element sends a seventh request message to the first entity, the seventh request message is used to request execution of the first federated learning task, and the seventh request message includes the first identity.

[0053] In some implementations, the third network element obtains the first entity and the first identity, including: the third network element receives the first entity and the first identity from the first network element, and the first network element supports initiation of the first federated learning task.

[0054] In some implementations, the third network element receives the first entity and the first identity from the first network element, including: the third network element receives a sixth request message from the first network element, the sixth request message is used to request creation or coordination of a first federated learning task, and the sixth request message includes the first entity and the first identity.

[0055] In some implementations, before the third network element receives the first entity and the first identity information from the first network element, the method also includes: the third network element sends at least one candidate entity and the identity information of at least one candidate entity to the first network element, the identity information of the at least one candidate entity is used to indicate the identity supported by the at least one candidate entity in the first federated learning task, and the at least one candidate entity includes the first entity.

[0056] In some implementations, before the third network element sends at least one candidate entity and identity information of at least one candidate entity to the first network element, the method also includes: the third network element receives a fourth request message from the first network element, the fourth request message is used to request preparation for execution of the first federated learning task; the third network element obtains at least one candidate entity and identity information of at least one candidate entity; the third network element sends an eighth request message to at least one candidate entity, the eighth request message is used to request at least one candidate entity to execute the first federated learning task, the eighth request message includes the identity information of at least one candidate entity; the third network element receives an eighth response message from at least one candidate entity, the eighth response message is used to indicate that at least one candidate entity agrees to support execution of the first federated learning task with the identity indicated by the identity information of the candidate entity.

[0057] In some implementations, the eighth request message also includes information about the third network element, and the eighth response message is further used to indicate consent for the third network element to negotiate the first federated learning task.

[0058] In some implementations, the third network element obtains at least one candidate entity and identity information of at least one candidate entity, including: the third network element receives at least one candidate entity and identity information of at least one candidate entity from the second network element, and the second network element supports discovering an entity that performs the first federated learning task.

[0059] In some implementations, the third network element receives at least one candidate entity and identity information of at least one candidate entity from the second network element, including: the third network element sends a third request message to the second network element, and the third request message is used to obtain an entity that supports executing the first federated learning task; the third network element receives a third response message from the second network element, and the third response message includes at least one candidate entity and identity information of at least one candidate entity.

[0060] In some implementations, before the third network element receives the first entity and the first identity from the first network element, the method also includes: the third network element receives a fifth request message from the first network element, the fifth request message being used to request the third network element to coordinate the first entity to perform the first federated learning task; the third network element sends a fifth response message to the first network element, the fifth response message being used to indicate that the third network element agrees to coordinate the first entity to perform the first federated learning task.

[0061] The beneficial effects of the third aspect and certain implementation methods mentioned above can be referred to the relevant description of the first aspect, and will not be repeated here.

[0062] In a fourth aspect, a communication device is provided, which can be used for a first network element and may include modules or units corresponding to the methods / operations / steps / actions described in the first aspect. The modules or units may be hardware circuits, software, or a combination of hardware circuits and software.

[0063] In some implementations, the device includes: a processing unit for determining a first entity and a first identity, the first identity being used to indicate the identity of the first entity in performing a first federated learning task; a transceiver unit for sending a first request message to the first entity, the first request message being used to request the first entity to perform the first federated learning task with the first identity.

[0064] The transceiver unit can perform the receiving and sending processing in the aforementioned first aspect and its possible implementations, and the processing unit can perform other processing except receiving and sending in the aforementioned first aspect and its possible implementations.

[0065] In the fifth aspect, a communication device is provided, which can be used for a second network element and may include modules or units corresponding to the methods / operations / steps / actions described in the second aspect. The modules or units may be hardware circuits, software, or a combination of hardware circuits and software.

[0066] In some implementations, the device includes: a transceiver unit for receiving a third request message, the third request message is used to obtain an entity that supports executing the first federated learning task, and the second network element supports discovering an entity that executes the first federated learning task; a processing unit for determining at least one candidate entity and identity information of at least one candidate entity, the identity information of at least one candidate entity is used to indicate the identity supported by at least one candidate entity in the first federated learning task; the transceiver unit is also used to send a third response message, the third response message includes at least one candidate entity and identity information of at least one candidate entity.

[0067] The transceiver unit can perform the receiving and sending processing in the aforementioned second aspect and its possible implementations. Optionally, the device also includes a processing unit, which can perform other processing in addition to receiving and sending in the aforementioned second aspect and its possible implementations.

[0068] In the sixth aspect, a communication device is provided, which can be used for a third network element and may include modules or units corresponding to the methods / operations / steps / actions described in the third aspect. The modules or units may be hardware circuits, software, or a combination of hardware circuits and software.

[0069] In some implementations, the device includes: a processing unit, used to obtain a first entity and a first identity, the first identity is used to indicate the identity of the first entity in performing a first federated learning task, and a third network element supports coordinating the first entity to perform the first federated learning task; a transceiver unit, used to send a seventh request message to the first entity, the seventh request message is used to request the execution of the first federated learning task, and the seventh request message includes the first identity.

[0070] The transceiver unit can perform the receiving and sending processing in the aforementioned third aspect and its possible implementations. Optionally, the device also includes a processing unit, which can perform other processing in addition to receiving and sending in the aforementioned third aspect and its possible implementations.

[0071] In the seventh aspect, a communication device is provided, comprising at least one processor, wherein the at least one processor is used to execute computer programs or instructions, and / or, through logic circuits, so that the communication device performs a method as in any aspect of the first to third aspects, or any possible implementation of these aspects.

[0072] In certain implementations, at least one processor is coupled to at least one memory, and the at least one memory stores the computer program or instructions. Optionally, the communication device further includes the at least one memory. Optionally, the at least one processor and the at least one memory are integrated.

[0073] In an eighth aspect, a chip is provided, comprising a processor and a communication interface, the communication interface being used to receive information and / or data to be processed and to send the information and / or data to be processed to the processor, the processor being used to process the information and / or data to be processed, so that a communication device in which the chip is installed executes a method as in any one of the first to third aspects, or any possible implementation of these aspects.

[0074] In a ninth aspect, a computer-readable storage medium is provided, in which computer instructions are stored. When the computer instructions are executed on a computer, the method of any one of the first to third aspects, or any possible implementation of these aspects, is implemented.

[0075] In a tenth aspect, a computer program product is provided, which includes a computer program code. When the computer program code is run on a computer, the method in any aspect from the first to the third aspect, or any possible implementation of these aspects, is implemented.

[0076] In an eleventh aspect, a communication system is provided, comprising a communication device according to any one or more of the fourth to sixth aspects.

[0077] Among them, the technical effects of the technical solutions of the fourth to eleventh aspects can refer to the description of the corresponding technical effects of the first to third aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] FIG1 is a schematic diagram of a network architecture applicable to an embodiment of the present application;

[0079] FIG2 is a flow chart of a method for obtaining an entity for performing federated learning;

[0080] FIG3 is a flow chart of a method for executing federated learning;

[0081] FIG4 is a flow chart of a communication method provided in an embodiment of the present application;

[0082] FIG5 is a flow chart of another communication method provided in an embodiment of the present application;

[0083] FIG6 is a flow chart of another communication method provided in an embodiment of the present application;

[0084] FIG7 is a flow chart of another communication method provided in an embodiment of the present application;

[0085] FIG8 is a flow chart of another communication method provided in an embodiment of the present application;

[0086] FIG9 is a schematic diagram of a communication device provided in an embodiment of the present application;

[0087] FIG10 is a schematic diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0088] The technical solution in this application will be described below with reference to the accompanying drawings.

[0089] The technical solutions provided in this application can be applied to various communication systems, such as new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, etc. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0090] In a communication system, the part operated by an operator may be referred to as a public land mobile network (PLMN), or as an operator network, etc. PLMN is a network established and operated by the government or an operator approved by it for the purpose of providing land mobile communication services to the public. It is mainly a public network in which mobile network operators (MNOs) provide mobile broadband access services to users. The PLMN described in the embodiments of the present application may specifically be a network that complies with the standards of the 3rd Generation Partnership Project (3GPP), referred to as a 3GPP network. 3GPP networks generally include but are not limited to fifth-generation mobile communication (5th-generation, 5G) networks, fourth-generation mobile communication (4th-generation, 4G) networks, and other future communication systems, such as sixth-generation mobile communication (6th-generation, 6G) networks.

[0091] For ease of description, the embodiments of the present application will be described using PLMN or 5G network as an example.

[0092] Figure 1 is a schematic diagram of a network architecture 100, using the 5G network architecture based on a service-based architecture (SBA) in a non-roaming scenario as defined in the 3GPP standardization process as an example. As shown in Figure 1 , the network architecture may include a terminal device component, a data network (DN) component, and a carrier network (PLMN) component. The carrier network PLMN component may include, but is not limited to, a (radio) access network (R)AN) 120 and a core network (CN) component.

[0093] The following is a brief description of the functions of the network elements in each part.

[0094] The terminal device portion may include a terminal device 110, which is a device that provides voice and / or data connectivity to the user. The terminal device 110 may also be referred to as a user equipment UE. The terminal device 110 in this application is a device with wireless transceiver functions, which can communicate with one or more core network (CN) devices via an access network device (or also referred to as an access device) in a (radio) access network (R)AN 120. The terminal device 110 may also be referred to as an access terminal, terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, user agent or user device, etc. The terminal device 110 may be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it may also be deployed on water (such as a ship, etc.); it may also be deployed in the air (such as an airplane, balloon and satellite, etc.). The terminal device 110 may be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a smartphone, a mobile phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), or the like. Alternatively, the terminal device 110 may be a handheld device with wireless communication capabilities, a computing device, or other device connected to a wireless modem, an in-vehicle device, a wearable device, an unmanned aerial vehicle (UAV), or a terminal in the Internet of Things (IoT), the Internet of Vehicles (IoV), any terminal in a 5G network or future networks, a relay user device, or a terminal in a future evolving 6G network. The relay user device may be, for example, a 5G residential gateway (RG). For example, the terminal device 110 may be a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in telemedicine, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, or a wireless terminal in a smart home. The terminal device here refers to a 3GPP terminal. The embodiments of the present application do not limit the type or category of terminal devices. For ease of explanation, the present application will use UE to represent terminal devices as an example for explanation.

[0095] (R)AN 120 may include one or more access network elements or access network devices, and the interface between the access network device and the terminal device may be a Uu interface (or air interface, that is, the messages exchanged between the access network device and the terminal device may be called air interface messages). Of course, in future communications, the interface name may remain unchanged or may be replaced by other names, and this application is not limited to this. (R)AN 120 is a device that provides wireless communication functions for the terminal device 110, which can connect the terminal device to a node or device of a wireless network, and may also be called a network device. The above-mentioned RAN may be a 3GPP-related cellular system, such as a 5G mobile communication system, or a future-oriented evolution system (such as a 6G mobile communication system). RAN may also be an open radio access network (open RAN, O-RAN or ORAN), a cloud radio access network (CRAN), or a wireless fidelity (WiFi) system. (R)AN 120 can be regarded as a subnetwork of the operator network, and is an implementation system between the service node in the operator network and the terminal device 110. For example, the terminal device 110 can connect to a service node of the operator network through the (R)AN 120 to obtain services provided by the service node. The (R)AN 120 includes, but is not limited to, a next generation node base station (gNB) in a 5G system, an evolved node B (eNB) in long term evolution (LTE), a radio network controller (RNC), a node B (NB), a base station controller (BSC), a base transceiver station (BTS), a home base station (e.g., home evolved node B or home node B, HNB), a base band unit (BBU), a transmitting and receiving point (TRP), a transmitting point (TP), a small base station device, a mobile switching center, or network equipment in a future network.The access network device may also be a module or unit that performs the functions of a base station, such as a centralized unit (CU) and a distributed unit (DU); in a possible network structure, the CU may be used to support communications under protocols such as radio resource control (RRC), packet data convergence protocol (PDCP), and service data adaptation protocol (SDAP); and the DU may be used to support communications under radio link control (RLC) layer protocols, media access control (MAC) layer protocols, and physical layer protocols. The embodiments of the present application do not limit the specific technology and specific device form adopted by the access network device. In systems using different wireless access technologies, the names of devices having access network device functions may be different. For the convenience of description, in all embodiments of the present application, the above-mentioned devices that provide wireless communication functions for the terminal device 110 are collectively referred to as access network devices or RAN for short. It should be understood that this document does not limit the specific type of access network device.

[0096] In different systems, CU (including CU-CP or CU-UP), or DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in the O-RAN system, CU may also be called O-CU (Open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, this application uses CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0097] The CN part may include but is not limited to the following network functions (NF): user plane function (UPF) 130, network exposure function (NEF) 131, network function repository function (NRF) 132, policy control function (PCF) 133, unified data management function (UDM) 134, unified data repository function (UDR) 135, network data analytics function (NWDAF) 136, application function (AF) 141, authentication server function (AUSF) 137, access and mobility management function (AMF) 138, and session management function (SMF) 139.

[0098] The data network DN 140, also called a packet data network (PDN), is typically a network outside the operator's network, such as a third-party network.

[0099] The following is a brief description of the NF functions included in CN.

[0100] 1. UPF 130 is a gateway provided by the operator and serves as the gateway for communication between the operator network and DN 140. UPF 130 network functions include packet routing and transmission, packet detection, service usage reporting, Quality of Service (QoS) processing, legal monitoring, uplink packet detection, downlink packet storage, and other user-plane related functions. For example, it is responsible for forwarding and receiving user data in terminal device 110. User data can be received from DN 140 and transmitted to terminal device 110 via access network device 120; UPF 130 can also receive user data from terminal device 110 via access network device 120 and forward it to DN 140. The transmission resources and scheduling functions in UPF 130 that provide services to terminal device 110 are managed and controlled by SMF 139.

[0101] 2. NEF 131 is a control plane function provided by the operator. It mainly enables third parties to use the services provided by the network, supports the network to open its capabilities, event and data analysis, provide PLMN security configuration information from external applications, and convert interactive information within and outside the PLMN.

[0102] 3. NRF 132 is a control plane function provided by the operator, which can be used to maintain real-time information of network functions and services in the network.

[0103] 4. PCF 133 is a control plane function provided by the operator. It primarily supports providing a unified policy framework to control network behavior, provides policy rules to the control layer network functions, and is responsible for obtaining user subscription information related to policy decisions. For example, PCF 133 can be divided into two different PCFs: UE-PCF and AMF-PCF.

[0104] 5. UDM 134 is a control plane function provided by the operator and is responsible for storing information such as the subscriber permanent identifier (SUPI), the generic public subscription identifier (GPSI), and credentials of subscribers in the operator's network.

[0105] 6. UDR 135 is a control plane function provided by the operator. It provides the UDM with the function of saving and retrieving subscription data, the PCF with the function of saving and retrieving policy data, and the user's NF group ID information.

[0106] 7. NWDAF 136 is a control plane function provided by the operator, with functions such as data collection, model training, data analysis, and model reasoning. The NWDAF network element, which includes the analytics logical function (AnLF), can be used to infer and derive analysis information and expose analysis services, where analysis can refer to statistical information and / or predictions generated or provided based on the request of an analysis consumer. The NWDAF network element, which includes the model training logical function (MTLF), can be used to train machine learning (ML) models or artificial intelligence (AI) models and expose new training services, such as providing trained AI models or ML models to AnLF. In this application, for the process of obtaining model-related data, AnLF can serve as a data producer network element, denoted as NFp; MTLF can serve as a data consumer network element, denoted as NFc. Currently, AnLF can request a model from MTLF through a model subscription (MLModelProvision_Subscribe) service or message. The model can be obtained by MTLF training based on model-related data (such as samples). In addition, AnLF can use a data set tag to specify a data set, which can contain data related to a model. Therefore, a data set tag can be used to label a data set, that is, to label model-related data. The data set tag can also be understood as an index of the data set or an index of the data. MTLF can act as a consumer network element of model-related data to obtain data stored in the data store by the data producer network element, where the data producer network element can be AnLF, and the data store network element can be a network element used to store data such as the analytics data repository functional (ADRF). In this application, relevant data can be understood as data used for vertical federated learning tasks, such as input data, training data, inference data, or sample data.

[0107] 8. AF 141 is a control plane function provided by the operator. It mainly provides corresponding services by interacting with other NFs in the PLMN, such as providing roaming UE with visitor network selection information, guiding the routing of data flows, and accessing NEF 131.

[0108] 9. AUSF 137 is a control plane function provided by the operator, and is usually used for level 1 authentication, i.e., authentication between the terminal device 110 (subscriber) and the operator's network.

[0109] 10. AMF 138 is a control plane network function provided by the operator network, responsible for access control and mobility management of the terminal device 110 accessing the operator network, such as mobility status management, allocation of user temporary identity, authentication and authorization of users, etc.

[0110] 11. SMF 139 is a control plane network function provided by the operator network. It is responsible for managing the protocol data unit (PDU) sessions of the terminal device 110 (including session establishment, modification, and release). This function is used for selecting and reselecting user plane function network elements, allocating Internet Protocol (IP) addresses to the terminal device, and controlling quality of service (QoS). A PDU session is a channel for transmitting PDUs. Terminal devices use PDU sessions to exchange PDUs with the DN 140. The SMF network function 139 is responsible for establishing, maintaining, and deleting PDU sessions. The SMF network function 139 includes session management (e.g., session establishment, modification, and release, including tunnel maintenance between the user plane function (UPF) 130 and the (R)AN 120), selection and control of the UPF network function 130, service and session continuity (SSC) mode selection, roaming, and other session-related functions.

[0111] It is understood that the above network elements or functions can be physical entities in hardware devices, software instances running on dedicated hardware, or virtualized functions instantiated on a shared platform (e.g., a cloud platform). Simply put, an NF can be implemented by hardware or software.

[0112] In Figure 1, Nnef, Nnrf, Npcf, Nudm, Nudr, Nnwdaf, Naf, Nausf, Namf, Nsmf, N1, N2, N3, N4, and N6 are interface serial numbers. For example, the meaning of the above interface serial numbers can be found in the meaning defined in the 3GPP standard protocol, and this application does not limit the meaning of the above interface serial numbers. It should be noted that the interface name between the various network functions in Figure 1 is only an example. In a specific implementation, the interface name of the system architecture may also be other names, which is not limited by this application. In addition, the name of the message (or signaling) transmitted between the above network elements is only an example and does not constitute any limitation on the function of the message itself.

[0113] It should be noted that in the architecture shown in Figure 1, the interface between the (R)AN and CN can also be called the NG interface (not shown in the figure), and the (R)AN and CN are connected via the NG interface. The NG interface can include the NG-C interface and the NG-U interface. The NG-C interface is a control plane interface, connecting the (R)AN and AMF, and is used to transmit control plane data; the NG-U interface is a user plane interface, connecting the (R)AN and UPF, and is used to transmit user plane data.

[0114] It should be understood that the above network architecture 100 is only described from the perspective of a service-based architecture. In this service-based architecture, the PLMN can combine some or all network functions in an orderly manner according to specific scenario requirements, realizing customized network capabilities and services, thereby deploying dedicated networks for different services, that is, realizing 5G network slicing. Network slicing technology enables operators to respond to customer needs more flexibly and quickly, and supports flexible allocation of network resources.

[0115] For ease of explanation, in the embodiments of the present application, network functions (such as NEF 131...SMF 139) are collectively referred to as NFs. That is, the NFs described later in the embodiments of the present application can be replaced by any network function. In addition, in the embodiments of the present application, the session management function SMF 139 is referred to as SMF, and the terminal device 110 is referred to as UE. That is, the SMFs described later in the embodiments of the present application can be replaced by session management functions, and the UE can be replaced by a terminal device. Figure 1 only schematically illustrates some network functions, and the NFs described later are not limited to the network functions shown in Figure 1.

[0116] It should be understood that the AMF, SMF, UPF, NEF, AUSF, NRF, PCF, and UDM shown in Figure 1 can be understood as network elements used to implement different functions in the core network, for example, they can be combined into network slices as needed. These core network network elements can be independent devices or integrated into the same device to implement different functions. This application does not limit the specific form of the above network elements.

[0117] It should also be understood that the above naming is defined only to facilitate the distinction between different functions and should not constitute any limitation to this application. This application does not exclude the possibility of adopting other naming in 5G networks and other future networks. For example, in a 6G network, some or all of the above network elements may continue to use the terminology used in 5G, or may adopt other names.

[0118] To facilitate understanding, the following first introduces relevant terms, concepts, or technologies that may be involved in the embodiments of this application:

[0119] 1. Federated learning;

[0120] Federated learning (FL) is a distributed machine learning approach in which multiple participants exchange model parameters through secure mechanisms, without interacting or sharing original training data, to achieve collaborative training. In other words, FL is an encrypted distributed machine learning technology. FL fully leverages the data and computing power of participating parties, enabling them to collaboratively build universal, robust machine learning models without sharing data. Therefore, FL can effectively help multiple organizations utilize data and conduct learning and modeling while meeting user privacy, data security, and government regulations. In other words, FL aims to enable the sharing of knowledge and parameters without exchanging any of their own data.

[0121] Federated learning includes horizontal federated learning and vertical federated learning. Amidst increasingly stringent data regulation, federated learning can address key issues such as data ownership, data privacy, data access rights, and access to heterogeneous data. A horizontal row in a data matrix represents a training example, and a vertical column represents a data feature. Horizontal federated learning combines multiple rows of samples with the same feature from multiple participants, meaning that the training data for each participant is partitioned horizontally. Horizontal federated learning, also known as feature-aligned federated learning, means that the data features of the participants are aligned. Horizontal federated learning can increase the total number of training examples. VFL, a machine learning technique, can be used to address model training and inference when participants are reluctant to share raw data. It is suitable for situations where there is significant overlap in the identification (ID) of participant training samples but little overlap in their data features. VFL combines the different data features of common samples from multiple participants for federated learning, meaning that the training data for each participant is partitioned vertically. Vertical federated learning combines the different data features of common samples from multiple participants for federated learning, meaning that the training data for each participant is partitioned vertically. Vertical federated learning is also called sample-aligned federated learning, that is, the training samples of the participants are aligned. Vertical federated learning can increase the feature dimension of the training data.

[0122] Federated learning has already been applied in industry. For example, Google uses it in its GBoard project, and WeBank's FATE federated learning framework is a prime example of the practical application of the aforementioned federated learning method using a parameter server architecture. Both require high consistency across the various devices involved in learning, consistent model structures, and aligned data.

[0123] 2. Analysis ID;

[0124] An analysis identifier can be used to indicate an analysis business, or analysis service (or simply service). This service is associated with a model, meaning the model can be used to execute the service. Alternatively, the analysis identifier is associated with the model, meaning the model is used to execute the service associated with the analysis identifier.

[0125] Alternatively, it can be understood that the MTLF is associated with an analysis identifier, that is, the model support provided by the MTLF is used to execute the service corresponding to the analysis identifier. For example, the MTLF can be associated with one or more analysis identifiers. It can be understood that the MTLF can provide a model for the service corresponding to each of the one or more analysis identifiers. For example, MTLF1 is associated with analysis identifier 1 and analysis identifier 2, that is, MTLF1 corresponds to analysis identifier 1 and analysis identifier 2. Then, MTLF1 can provide a model for the service corresponding to analysis identifier 1, and a model for the service corresponding to analysis identifier 2.

[0126] 3. Interoperability indicator;

[0127] For example, the interoperability identifier can correspond to the MTLF, or to the analysis identifier, or to the analysis identifier corresponding to the MLTF. Alternatively, the interoperability identifier can be described as being related to the MTLF, or the interoperability identifier is related to the analysis identifier. The interoperability identifier can also be called an interoperability indicator, a machine learning (ML) model interoperability identifier, or a model interoperability indicator.

[0128] The interoperability identifier includes a list of vendors, or is described as a list of NWDAF providers (or suppliers). The vendors in the vendor list are allowed to retrieve or use models provided by the MTLF. The interoperability identifier also indicates that the MTLF supports vendors requesting models provided by the MTLF for the NWDAF of the vendors in the vendor list. The interoperability identifier also indicates that the vendors in the vendor list are allowed to obtain models from the MTLF. The interoperability identifier also indicates that the MTLF allows the vendors in the vendor list to obtain models from the MTLF.

[0129] The interoperability identifier is a list of MTLF providers, for example, the interoperability identifier represents the manufacturer identifier, or the interoperability identifier is associated with the manufacturer identifier. The interoperability identifier can be associated with the analysis identifier, such as a one-to-one correspondence between the two, indicating that the MTLF allows the corresponding manufacturer or the MTLF included in the manufacturer to obtain the model corresponding to the analysis identifier, and / or indicates that the MTLF is allowed to interoperate with the AnLF on the model corresponding to the analysis identifier. Optionally, a MTLF may have one or more interoperability identifiers. If there are multiple interoperability identifiers, the multiple interoperability identifiers correspond to different analysis identifiers respectively. For example, MTLF NF ID 1 corresponds to analysis identifier 1 and analysis identifier 2, wherein the MTLF to which MTLF NF ID 1 belongs has interoperability identifier 1 and interoperability identifier 2, interoperability identifier 1 corresponds to analysis identifier 1, and interoperability identifier 2 corresponds to analysis identifier 2. Optionally, if the MTLF to which MTLF NF ID 1 belongs and the MTLF to which MTLF NF ID 2 belongs support interoperability, the MTLF to which MTLF NF ID 2 belongs may have interoperability identifier 1 and interoperability identifier 2, wherein interoperability identifier 1 corresponds to analysis identifier 1, and / or, interoperability identifier 2 corresponds to analysis identifier 2, that is, MTLFs of the same manufacturer may have the same interoperability identifier for the same analysis identifier. In addition, if the MTLF to which MTLF NF ID 1 belongs and the MTLF to which MTLF NF ID 2 belongs belong to different manufacturers, the MTLF to which MTLF NF ID 2 belongs may have interoperability identifier 3 and interoperability identifier 4, wherein interoperability identifier 3 corresponds to analysis identifier 1, and / or, interoperability identifier 4 corresponds to analysis identifier 2, that is, MTLFs of the same manufacturer may have different interoperability identifiers for the same analysis identifier.

[0130] Exemplarily, analysis identifier 1 is associated with model 1, i.e., model 1 is used to execute the service corresponding to analysis identifier 1, and analysis identifier 1 is associated with interoperability identifier 1, i.e., model 1 is associated with interoperability identifier 1. Assume that interoperability identifier 1 includes the identifier of manufacturer 1 and the identifier of manufacturer 2, i.e., model 1 can be provided to manufacturer 1 and manufacturer 2 for use. Alternatively, it can be understood that if the manufacturer of the NWDAF is manufacturer 1 or manufacturer 2, then the NWDAF can use model 1.

[0131] For example, a MTLF may have one or more interoperability identifiers. If there are multiple interoperability identifiers, the multiple interoperability identifiers may correspond to different analysis identifiers. For example, MTLF1 corresponds to analysis identifier 1 and analysis identifier 2, where interoperability identifier 1 corresponds to analysis identifier 1 and interoperability identifier 2 corresponds to analysis identifier 2.

[0132] 4. Model producer;

[0133] A model producer is the entity that produces the model, or is authorized to provide model information to other entities based on network configuration. Consumers can obtain the model based on the model information. Model information includes, but is not limited to, the uniform resource locator (URL) of the model file and the model itself.

[0134] 5. Manufacturer's logo;

[0135] The vendor ID of the vendor can also be expressed as network element information, identifying the vendor or manufacturer of the network element. For example, Vendor ID1 identifies the vendor of the NWDAF network element.

[0136] The vendor identifier can be used to identify a device manufacturer. A vendor identifier can correspond to one or more NWDAF device identifiers. For example, NWDAF NF ID 1 and NWDAF NF ID 2 can both correspond to vendor identifier 1. This means that the MTLF to which MTLF NF ID 1 belongs and the MTLF to which MTLF NF ID 2 belongs belong to the same vendor, whose vendor identifier is vendor identifier 1.

[0137] The above briefly explains the terms involved in this application, which will not be repeated in the following embodiments. In addition, the above explanation of the terms is only for the purpose of facilitating understanding and does not limit the scope of protection of the embodiments of this application.

[0138] Figure 2 is a flow chart of a method for obtaining a federated learning entity. As shown in Figure 2, the method includes the following steps. For details not provided, please refer to existing protocols. Steps S201-S205 describe the NWDAF registration process, steps S206-S208 describe the client NWDAF discovery process, and steps S209-S212 describe the server NWDAF's selection process for the client NWDAF to execute federated learning.

[0139] S201 , the server NWDAF sends a registration request message #1 to the NRF. Correspondingly, the NRF receives the registration request message #1 from the server NWDAF, where the registration request message #1 is used to request registration with the network.

[0140] Exemplarily, the registration request message #1 may be an Nnrf_NFManagement_NFRegister_request message, and the registration request message #1 includes configuration parameters (e.g., Server NWDAF profile), where the configuration parameters include one or more of the following: NWDAF NF type, analysis ID(s), address information of the server NWDAF, service area, FL capability type information (e.g., FL server), or a time interval for the server NWDAF to support FL.

[0141] S202 , the client NWDAF sends a registration request message #2 to the NRF. Correspondingly, the NRF receives the registration request message #2 from the client NWDAF, where the registration request message #2 is used to request registration with the network.

[0142] Exemplarily, the registration request message #2 may be an Nnrf_NFManagement_NFRegister_request message, and the registration request message #2 includes configuration parameters (e.g., Client NWDAF profile), where the configuration parameters include one or more of the following: NWDAF NF type, analysis ID(s), address information of the client NWDAF, service area, FL capability type information (e.g., FL client), or a time interval for the client NWDAF to support FL.

[0143] It should be understood that both the server NWDAF and the client NWDAF are NWDAFs that include MTLF and can participate in federated learning training.

[0144] Optionally, the present application does not limit the number of client NWDAFs, for example, one or more, NWDAF 1, ..., NWDAF N, where N is an integer greater than or equal to 1.

[0145] S203, the NRF stores configuration parameters of the server NWDAF and the client NWDAF.

[0146] Illustratively, the NRF stores the Server NWDAF profile and the Client NWDAF profile.

[0147] S204 , the NRF sends a registration response message # 2 to the client NWDAF. Correspondingly, the client NWDAF receives the registration response message # 2 from the NRF.

[0148] S205 , the NRF sends a registration response message # 1 to the server NWDAF. Correspondingly, the server NWDAF receives the registration response message # 1 from the NRF.

[0149] S206: The server NWDAF sends a discovery request message to the NRF. Correspondingly, the NRF receives the discovery request message from the server NWDAF.

[0150] Exemplarily, the server NWDAF discovers available services in the network by sending a discovery request message to the NRF. For example, the server NWDAF invokes Nnrf_NFDiscovery_Request from a properly configured NRF in the same PLMN, and carries the NF type of the desired NF instance in the Nnrf_NFDiscovery_Request, and optionally, one or more of the following: the desired service name, the NF type of the server NWDAF, and the desired target NF location.

[0151] S207, NRF authorization.

[0152] Exemplarily, the NRF authorizes the Nnrf_NFDiscovery_Request of step S206. For example, the NRF determines whether to allow the server NWDAF to discover the desired NF instance based on the profile of the desired NF / NF service and the type of the server NWDAF. If the desired NF instance or NF service instance is deployed in a certain network slice, the NRF authorizes the discovery request based on the discovery configuration of the network slice, for example, the desired NF instance can only be discovered by NFs in the same network slice.

[0153] S208 , the NRF sends a discovery response message to the server NWDAF. Correspondingly, the server NWDAF receives the discovery response message from the NRF.

[0154] The discovery response message may carry one or more client NWDAFs, such as client NWDAF 1, ..., client NWDAF N.

[0155] Exemplarily, if the NRF authorizes, the NRF determines the set of matching NF instances based on the Nnrf_NFDiscovery_Request and the NRF internal policy, and sends the NF profile of the NF instance to the client NWDAF. Exemplarily, the discovery response message can be the Nnrf_NFDiscovery_Response message, and the NF profile of each NF instance can be sent to the server NWDAF via the Nnrf_NFDiscovery_Response message.

[0156] Optionally, if the server NWDAF carries the desired target NF location in step S206, the NRF should not restrict the set of discovered NF instances or NF service instances to the target NF location. For example, if no NF instance or NF service instance can be found for the preferred target NF location, the NRF may provide an NF instance or NF service instance whose location is not the preferred target NF location.

[0157] S209 , the server NWDAF sends a federated learning preparation request message to one or more client NWDAFs. Correspondingly, the one or more client NWDAFs receive the federated learning preparation request message from the server NWDAF.

[0158] Exemplarily, the federated learning preparation request message may be a Federated Learning preparation request message, such as the server NWDAF sending a federated learning preparation request to the FL client NWDAF using the Nnwdaf_MLModel training_subscription or Nnwdaf_MLModel training information_request service with the ML preparation flag, to check whether the client NWDAF can meet the ML model training requirements (such as analysis ID, ML model interoperability information), available data requirements (event ID list of local data used for training, available data requirements may also include dataset statistical properties, time window of data samples and minimum number of data samples), or availability time requirements (time span required for the FL process), etc.

[0159] S210: One or more client NWDAFs determine whether to join federated learning.

[0160] For example, the client NWDAF(s) checks whether it can meet the ML model training requirements and / or, if the model information is provided in the federated learning preparation request message in step S209, the client NWDAF(s) also needs to check whether it can successfully download the model and decide whether to join the federated learning process based on the implementation. Example criteria used by the client NWDAF(s) may be based on its availability, computing and communication capabilities, and ML model interoperability information.

[0161] S211 , one or more client NWDAFs send a federated learning preparation response message to the server NWDAF. Correspondingly, the server NWDAF receives the federated learning preparation response message from the one or more client NWDAFs.

[0162] Exemplarily, the federated learning preparation response message may be a Federated Learning preparation response message, for example, the client NWDAF(s) calls the Nnwdaf_MLModel training_subscription response service operation or the Nnwdaf_MLModel training information_request response service operation to indicate whether to join the federated learning process. If the federated learning process cannot be joined, the client NWDAF(s) may carry a reason value in the federated learning preparation response message, for example, the client NWDAF(s) is currently unable to support federated learning, or the client NWDAF(s) is currently overloaded, etc.

[0163] S212: The server NWDAF selects one or more client NWDAFs that support executing federated learning.

[0164] Exemplarily, the server NWDAF selects or finally determines one or more client NWDAFs that support the execution of federated learning based on the federated learning preparation response messages sent by one or more client NWDAFs received in step S211. The specific process of one or more client NWDAFs executing federated learning can refer to the scheme shown in Figure 3 below.

[0165] FIG3 is a flow chart of a method for executing federated learning. As shown in FIG3 , the method includes the following steps. For parts not described in detail, reference may be made to existing protocols.

[0166] S301: A consumer sends a subscription request message #1 to a server NWDAF. In response, the server NWDAF receives the subscription request message #1 from the consumer. The subscription request message #1 is used to subscribe to ML model provisioning or training.

[0167] Exemplarily, a consumer (e.g., NWDAF including AnLF, or NWDAF including MTLF) uses the Nnwdaf_MLModelProvision service to send a subscription request message #1, such as subscription request for ML model provisioning / training, to the server NWDAF to retrieve the ML model.

[0168] The subscription request message #1 includes one or more of the following: an analysis ID, an ML model metric (e.g., ML model accuracy), an accuracy reporting interval, and a predetermined status (an ML model accuracy threshold or a time when an ML model is required). It should be understood that the ML model accuracy threshold can be used to indicate the target ML model accuracy during training. When the ML model accuracy threshold is reached during training, the server NWDAF can stop the training process. If the consumer provides a time when an ML model is required, the server NWDAF can consider this information to determine the maximum response time of its client NWDAF.

[0169] S302: The server NWDAF determines the client NWDAF(s).

[0170] The specific implementation can refer to the related description of the above method 200, which will not be repeated here for the sake of brevity. For example, the client NWDAF(s) determined by the server NWDAF include: client NWDAF1, ..., client NWDAF N.

[0171] S303 , the server NWDAF sends a subscription request message # 2 to the client NWDAF(s). Correspondingly, the client NWDAF(s) receives the subscription request message # 2 from the server NWDAF.

[0172] Exemplarily, the subscription request message #2 may be Nnwdaf_MLModelTraining_Subscribe. For example, the server NWDAF sends Nnwdaf_MLModelTraining_Subscribe or Nnwdaf_MLModelTrainingInfo_Request to the client NWDAF(s) to perform local model training.

[0173] The subscription request message #2 may carry one or more of the following: initial federated learning parameter provisioning, ML model metrics, initial ML model, or maximum response time, where the maximum response time refers to the maximum response time for the client NWDAF to report temporary local ML model information to the server NWDAF.

[0174] S304: Optionally, the client NWDAF(s) collects data.

[0175] Illustratively, if the client NWDAF does not already have local data available, each client NWDAF may collect its local data from the NF (data provider) using the current mechanism in TS 23.288.

[0176] S305 , the client NWDAF(s) sends a subscription response message # 2 to the server NWDAF. Correspondingly, the server NWDAF receives the subscription response message # 2 from the client NWDAF(s).

[0177] For example, subscription response message #2 may be Nnwdaf_MLModelTraining_Notify, which is used to report local model training information. For example, during the federated learning training process, each client NWDAF trains the ML model provided by the server NWDAF based on its own data and reports the temporary local ML model information to the server NWDAF in Nnwdaf_MLModelTraing_Notify or Nnwdaf_MLModelTraingInfo_Response.

[0178] Optionally, Nnwdaf_MLModelTraining_Notify or Nnwdaf_MLModelTrainingInfo_Response may also include local ML model metrics calculated by the client NWDAF(s) and training input data information (e.g., the area covered by the dataset, sampling ratio, maximum / minimum values ​​of each dimension of the data, etc.).

[0179] Optionally, the ML model is sent from the client NWDAF(s) to the server NWDAF during the federated learning training process. This is the information required by the server NWDAF to build an aggregate model based on the locally trained ML model. If the client NWDAF cannot complete the training of the temporary local ML model within the maximum response time provided by the server NWDAF, the client NWDAF(s) may send a delay event notification, including a delay event indication, an optional reason code (e.g., local ML model training failed, or more time is required for local ML model training), and the expected time for the client NWDAF to complete the training before the maximum response time elapses.

[0180] S306 , optionally, the server NWDAF sends a subscription request message # 3 to the client NWDAF(s). Correspondingly, the client NWDAF(s) receives the subscription request message # 3 from the server NWDAF.

[0181] Exemplarily, the subscription response message #3 may be Nnwdaf_MLModelTraining_Notify, carrying the extended response time and / or the current iteration round ID. For example, if the server NWDAF receives a notification / response from the client NWDAF(s) that the training cannot be completed within the maximum response time, the server NWDAF may send an ExtendedMaximumResponseTime_MLModelTrainingInfo_Request to the client NWDAF in Nnwdaf_MLModelTraining_Subscribe or Nnwdaf. Prior to this request, the client NWDAF needs to report the temporary local ML model information to the server NWDAF. Otherwise, the server NWDAF may instruct the client NWDAF to skip reporting for this iteration. The client NWDAF includes the current iteration round ID in the request message to indicate that this request is for modifying the training parameters of the current iteration round.

[0182] Optionally, the server NWDAF may notify the client NWDAF to stop ML model training by sending a termination request and report the current local ML model update.

[0183] S307: The client NWDAF performs model aggregation.

[0184] Illustratively, the client NWDAF aggregates all local ML model information retrieved in step S305 to update the global ML model. Optionally, the server NWDAF can also calculate global ML model metrics, for example, based on local ML model metrics or by applying the global model on a validation dataset (if available). The server NWDAF can update the global ML model each time the client NWDAF provides updated local ML model information, or the server NWDAF can decide to wait for local ML model information from all client NWDAFs before updating the global ML model.

[0185] If the server NWDAF provides a maximum response time for the client NWDAF to provide temporary local ML model information in step S303, or provides an extended maximum response time in step S306, the server NWDAF decides to wait for client NWDAFs that have not yet provided their temporary local ML models within the (extended) maximum response time, or to aggregate only the retrieved local ML model information instances to update the global ML model. The server NWDAF makes this decision based on the notification / response from the client NWDAF, or if no notification is received, based on the local configuration.

[0186] S308, optionally, the client NWDAF sends an update message to the consumer, and correspondingly, the consumer receives the update message from the client NWDAF, wherein the update message is used to indicate the current ML training status to the consumer.

[0187] Exemplarily, in response to step S301 , the client NWDAF sends a Nnwdaf_MLModelProvision_Notify message to the consumer to dynamically update the global ML model metrics to the consumer periodically (e.g., a certain number of training rounds or every 10 minutes) or when certain predetermined states are reached (e.g., the ML model accuracy threshold is reached or the training time expires).

[0188] S309: Optionally, the consumer sends a subscription request message #4 to the server NWDAF, and in response, the server NWDAF receives the subscription request message #4 from the consumer. The subscription request message #4 is used to modify the subscription to update or terminate.

[0189] For example, the consumer determines whether the current model can meet the requirements, for example, whether the global ML model metrics are satisfactory to the consumer, and decides to stop or continue the training process. The user can re-call the Nnwdaf_MLModelProvision_Subscribe service operation used in step S301 to stop or continue the training process.

[0190] S310, optionally, the server NWDAF updates or terminates the federated learning training process.

[0191] Exemplarily, the server NWDAF updates or terminates the current federated learning training process based on the subscription request message #4 sent by the consumer in step S309. Optionally, if the server NWDAF receives a request to stop the federated training process in step S309, the following steps S311 and S312 are skipped.

[0192] S311 , optionally, the server NWDAF sends aggregation model information to the client NWDAF(s), and correspondingly, the client NWDAF(s) receives the aggregation model information from the server NWDAF.

[0193] In other words, if the federated learning training process continues, the server NWDAF will determine the client NWDAF and send Nnwdaf_MLModelTraingInfo_Request including the aggregated ML model information to the selected client NWDAF(s) for the next round of federated training.

[0194] S312, optionally, the client NWDAF(s) updates the local model according to the aggregated model information.

[0195] Exemplarily, each client NWDAF updates its local ML model according to the aggregated ML model information distributed by the server NWDAF in step S311 .

[0196] It should be noted that the above steps S304-S312 are repeated until a training termination condition (for example, a maximum number of iterations, or a result of the loss function is lower than a threshold) is reached.

[0197] When the federated training process is completed, the server NWDAF requests the client NWDAF(s) to terminate the federated learning training process. In one implementation, the server NWDAF calls the Nnwdaf_MLModelTraing_Unsubscribe service with the reason code "The federated learning process has been completed" and optionally uses the final aggregated ML model information. The client NWDAF(s) then terminates local model training. If the final aggregated ML model information is received from the server NWDAF, the client NWDAF(s) may store the aggregated ML model information for further use.

[0198] Optionally, after the federated learning training process is completed, the client NWDAF can send Nnwdaf_MLModelProvision_Notify containing the global optimal ML model information to the consumer.

[0199] Currently, 5GC only supports horizontal federated learning, and there is no process mechanism for supporting vertical federated learning. In vertical federated learning, there are multiple types of participants, such as UEs, NWDAFs, or AFs. However, existing network element discovery mechanisms do not support discovering multiple NF types simultaneously in the same request message. Furthermore, in vertical federated tasks, different participants may have different label information. Models trained using these different label information may differ, resulting in inefficient execution of federated learning tasks. Therefore, how to effectively execute vertical federated learning tasks is a critical issue.

[0200] In view of this, the present application provides a communication method and a communication device, which sends a first identity to a first entity so that the first entity can perform a first federated learning task with the first identity, thereby effectively improving the execution efficiency of the first federated learning task.

[0201] The communication method provided by the embodiment of the present application will be described in detail below with reference to the accompanying drawings. The embodiment provided by the present application can be applied to a communication scenario where a transmitting device and a receiving device communicate, for example, it can be applied to the communication system shown in FIG1 above.

[0202] Figure 4 is a flow chart of a communication method 400 provided in an embodiment of the present application. As shown in Figure 4, the method flow can be executed by a first network element (for example, a task initiator), a second network element (for example, an entity discovery function network element), or a third network element (for example, a task coordinator), or it can also be executed by a chip or circuit of the first network element, the second network element, or the third network element, or it can also be implemented by a logic module or software that can realize all or part of the functions of the communication device, and the present application does not impose any restrictions on this. The following is an explanation with the execution subject being the first network element, the second network element, or the third network element as the execution subject. The method includes the following multiple steps, and the part not described in detail can refer to the above-mentioned existing protocol.

[0203] S410: The second network element receives a third request message.

[0204] The third request message is used to request an entity that supports executing the first federated learning task.

[0205] In this application, federated learning may be referred to as federated machine learning, joint learning, or federated learning.

[0206] Optionally, the first federated learning task can be replaced by other names such as the first federated learning, the first federated learning function, the first federated learning process, or the first federated learning activity, which is not limited in this application. In addition, obtaining an entity that supports the execution of the first federated learning task can be understood as obtaining an entity (or network element, device, or node, etc.) that has the ability to execute the first federated learning task, or in other words, the entity supports the execution of the first federated learning task (or activity, or process, etc.).

[0207] For example, the first federated learning task may include one or more of the following: network performance analysis, artificial intelligence (AI) model training, facial recognition, or cross-institutional medical data analysis and disease prediction. Alternatively, the federated learning task in the embodiments of this application may refer to a vertical federated learning task, for which specific interpretation please refer to the relevant description above.

[0208] In this application, the second network element supports discovering an entity that performs the first federated learning task, or in other words, the second entity supports providing information about an entity capable of performing the first federated learning task. For example, the second network element may be an NRF or NEF or other network entity. Optionally, for discovering an NF (e.g., NWDAF) type entity, the second network element may be an NRF or other network entity; for discovering an AF type entity, the second network element may be an NEF or other network entity.

[0209] It should be understood that the entity supporting the execution of the first federated learning task can be understood as: the entity supports the first federated learning task, or in other words, the entity has the ability to execute the first federated learning task. Exemplarily, the entity may include one or more of the following: NWDAF, AF, coordinator (i.e., an example of a third network element), or a network element or entity related to the first federated learning task. Optionally, the entity supporting the execution of the first federated learning task in this application can also be replaced by a network element or device supporting the execution of the first federated learning task.

[0210] Optionally, in one implementation, the second network element may receive a third request message from the first network element or the third network element, as shown in the following step S401 or S402 for details.

[0211] S401: A first network element sends a third request message to a second network element. Correspondingly, the second network element receives the third request message from the first network element.

[0212] In this embodiment of the present application, the first network element supports initiating the first federated learning task, or in other words, the first network element has the ability to initiate the first federated learning task. For example, the first network element can be a coordinator, an NWDAF, or an AF. Assuming that the first network element is AF1 and the second network element is an NRF, AF1 can send a third request message to the NRF to request an entity that supports network performance analysis.

[0213] S402, the third network element sends a third request message to the second network element, and correspondingly, the second network element receives the third request message from the third network element.

[0214] In an embodiment of the present application, the third network element supports coordinating the first entity to perform the first federated learning task, or in other words, the third network element has the ability to support coordinating the first entity to perform the first federated learning task. Optionally, the third network element may belong to the same network as the first network element or the second network element, for example, the third network element may be an NF, AF, or coordinator, or the third network element may be deployed in the same network as the first network element or the second network element, such as an external network, and the third network element may be a third-party entity, which is not limited in this application. Assuming that the third network element is a task coordinator and the second network element is an NRF, the task coordinator may send a third request message to the NRF to request an entity that supports network performance analysis.

[0215] Optionally, the third request message includes one or more of the following:

[0216] (1) Multiple types;

[0217] Among them, each of the multiple types corresponds to at least one candidate entity, and the type here can be understood as the type of the entity, for example, multiple network function NF types and / or multiple application function AF types. Exemplarily, the at least one candidate entity may include one or more of the following: AF, NF, or a third network element (for example, a task coordinator). The at least one candidate entity refers to a candidate entity capable of performing the first federated learning task. In an embodiment of the present application, the at least one candidate entity includes the first entity.

[0218] That is to say, the first network element or the third network element can improve the efficiency of the second network element in discovering at least one candidate entity by carrying multiple types in the third request message. That is to say, the second network element can provide at least one candidate entity corresponding to the multiple types. That is to say, the types of candidate entities provided by the second network element are diverse and not limited to one NF type or one AF type.

[0219] (2) The first group of logos;

[0220] The first group identifier is used to indicate a first federated learning group. Entities in the first federated learning group support execution of the first federated learning task. That is, the first federated learning group corresponds to the first federated learning task. For example, if the first federated learning group includes at least one candidate entity, then the first entity is included in the first federated learning group. Optionally, the entities in the first federated learning group and their identity information are preconfigured.

[0221] For example, the first group identifier is VFL ID1, and the information of the entities in the first federated learning group includes one or more of the following: AF identifier AF ID(s), AF instance instance(s) (for example, AF1, AF2), AF type(s), application service name(s) (Application Name), vendor equipment name Vendor ID(s), NF identifier NF ID(s), NF instance(s) (for example, NWDAF ID), or network identifier PLMN ID(s), where AF1 identifies a certain type of AF, or a specific AF.

[0222] In other words, by carrying VFL ID1 in the third message, the second network element can determine the first federated learning group, that is, determine the member information in the first federated learning group (including member identification, member type, member identity information, member capability information, and one or more of the federated learning tasks supported by the member), wherein the member information in the first federated learning group can be predefined or preconfigured.

[0223] Exemplarily, the first federated learning group may also be referred to as the first federated learning alliance, wherein the alliance member information (including one or more of alliance member identification, alliance member type, alliance member identity information, alliance member capability information, and federated learning tasks supported by alliance members) is provided.

[0224] In a possible case, information of entities in the first federated learning group is also included.

[0225] (3) first analysis identifier;

[0226] The first analysis identifier is used to indicate the first federated learning task.

[0227] Exemplarily, the first analysis identifier can be used to indicate a specific function or service that is related to the model, that is, the model can be used to perform the specific function or service, or in other words, the model supports the execution of the specific function or service corresponding to the first analysis identifier. Among them, the specific function or service can be face recognition or network performance analysis, etc. Taking the analysis ID as an example, the first federated learning group can correspond to one or more analysis IDs. For example, the analysis ID can indicate analysis services such as terminal anomaly detection, terminal session analysis (such as quality of service QoS analysis), etc.

[0228] (4) a first interoperability indicator;

[0229] The first interoperability identifier may correspond to the first analysis identifier and be used to indicate the first federated learning task.

[0230] (5) A task identifier corresponding to the first federated learning task, where the task identifier is used to identify the first federated learning task.

[0231] Optionally, the task identifier may be allocated by the first network element, or may be allocated by a third network element and then sent to the first network element, which is not limited in this application.

[0232] (6) Information on vertical federal alliances;

[0233] Exemplarily, the information of a vertical federation alliance can be used to identify or distinguish a vertical federation alliance (e.g., a first federated learning group). For example, the information of a vertical federation alliance can include one or more of an identifier of the vertical federation alliance (e.g., a first group identifier), an identifier of at least one analysis service corresponding to the vertical federation alliance (e.g., a first analysis identifier), an identifier of at least one AF corresponding to the vertical federation alliance, information of at least one network corresponding to the vertical federation alliance, or information of at least one equipment vendor corresponding to the vertical federation alliance. The identifier of the vertical federation alliance can be used to indicate or identify a vertical federation alliance, and can be referred to as an alliance ID (i.e., a first group ID, such as VFL ID1).

[0234] It can be understood that a vertical federation alliance (for example, the first federated learning group) can be a professional group composed of two or more members (or participants) with the goal of participating in a vertical federation of common activities or sharing each other's resources to achieve common results. Among them, any member can be a natural person, a company, an organization, a network element entity (such as an application function or service) or a network (such as a PLMN or CN), etc., or can be a combination of any of the above members. All members or some members in the alliance jointly participate in a vertical federation task (for example, the first federated learning task). Vertical federation tasks refer to tasks corresponding to vertical federated learning, and vertical federation tasks may include training and / or reasoning. Vertical federation tasks can also be called vertical federation activities or vertical federated learning tasks, or simply learning tasks or federation tasks, etc. In various embodiments of the present application, the multiple AFs included in the vertical federation alliance can maintain their respective data respectively, for example, each AF maintains terminal data of different terminals. Among them, the object of vertical federated learning can be a terminal.

[0235] For vertical federation tasks (e.g., the first federated learning task), the terminal data contained in each of the multiple AFs within the vertical federation alliance can serve as samples for the learning task. Vertical federation learning generally requires that members of the vertical federation use the same object data for training. Determining samples for a vertical federation task can be understood as each member of the vertical federation determining which object data to use together for the vertical federation task. For example, when the vertical federation task is terminal PDU session analysis, and the vertical federation members include the CN, AF1, and AF2, then the CN, AF1, and AF2 all need to use the same terminal data for the vertical federation task. AF1 and AF2 can be connected to the CN. For example, if the vertical federation members include a NF (such as an NWDAF) in a PLMN network, AF1, and AF2, then the NWDAF, AF1, and AF2 all need to use the same terminal data for the vertical federation task. AF1 and AF2 can be AFs within the PLMN network. For another example, the AF can be an AF outside the network, a trusted AF within the PLMN, or an untrusted AF. In other words, the relationship between the AF and the network is not restricted. It is understood that there is no limitation on the relationship between members in the vertical federation. In this application, AF can be an application provider, such as an application server (AS) or an application-related network element entity.

[0236] S420: The second network element determines at least one candidate entity and identity information of the at least one candidate entity.

[0237] The identity information of the at least one candidate entity is used to indicate the identity supported by the at least one candidate entity in the first federated learning task. The at least one candidate entity is a candidate entity capable of executing the first federated learning task. In this embodiment of the present application, the at least one candidate entity includes the first entity.

[0238] Exemplarily, the identities supported by the candidate entity in the first federated learning task include any one or more of the following:

[0239] (1) Supporting providing a label in a first federated learning task, where the label corresponds to the first federated learning task (which may be referred to as a primary participant), wherein the label is used in the first federated learning task, for example, for training and / or model evaluation of a model corresponding to the first federated task;

[0240] (2) Supporting the provision of labels and data in the first federated learning task (which can be called the main participant), wherein the data is used for the first federated learning task, for example, for training and / or reasoning of the model corresponding to the first federated task; optionally, the data granularity supported by the vertical federated alliance can be terminal granularity, alliance identification granularity, analysis identification granularity, alliance member granularity, etc. That is, it represents the granularity of the data used by the first federated learning task. For example, the terminal granularity indicates that the sample used by the first federated learning task is the data of the terminal within a certain range. For example, for the terminal granularity, the data granularity supported by the vertical federated alliance can be used to indicate that the sample used by the first federated learning task is the traffic data of the terminal on the AF or the business data of the terminal on the AF, etc.

[0241] (3) Supporting data provision in the first federated learning task (which can be called slave participants);

[0242] (4) Support and coordinate participants to complete the first federated learning task (can be called coordinator).

[0243] In the embodiments of this application, identity information can be understood as role information, or capability information. Supported identities can be understood as supported roles, where roles can be understood as logical roles in the first federated learning task, such as master participant, slave participant, or coordinator. Supported identities can also be understood as possessed capabilities, or capabilities corresponding to identities, including: the ability to provide federated training labels in the first federated learning task, and / or the ability to provide federated training data in the first federated learning task.

[0244] The following describes a specific implementation manner in which the second network element determines at least one candidate entity and identity information of at least one candidate entity.

[0245] In one implementation, the third request message itself may indicate the first federated learning task. For example, the third request message may be: the second network element may determine the first federated learning task and / or the first federated learning group based on the received third request message, and take some or all entities in the first federated learning group as candidate entities, thereby determining at least one candidate entity and identity information of at least one candidate entity.

[0246] In another implementation, the second network element determines the corresponding first federated learning group based on at least one of the first analysis identifier, the first group identifier, the vertical federal alliance information, or the first interoperability identifier carried in the third request message, and then determines at least one candidate entity and the identity information of at least one candidate entity from the first federated learning group.

[0247] In another implementation, the second network element selects entities of corresponding AF type and / or NF type from the first federated learning group based on the multiple types carried in the third request message, and uses the entities of AF type and / or NF type as candidate entities, thereby determining at least one candidate entity and identity information of at least one candidate entity.

[0248] Optionally, the at least one candidate entity may be understood as: at least one active entity in the first federated learning group, where the active entity may refer to: a member of the current first federated learning group that is capable of or supports participating in the first federated learning task.

[0249] Optionally, before executing step S410, the second network element obtains at least one candidate entity and identity information of at least one candidate entity in the first federated learning group.

[0250] In one example, at least one candidate entity in the first federated learning group and identity information of at least one candidate entity may be predefined, or configured by signaling or preconfigured. Predefinition may include predefinition, such as protocol definition. Preconfiguration may be implemented by pre-saving, in the second network element, a corresponding code, table, string, or other method for indicating the candidate entities in the first federated learning group and their identity information. This application does not limit the specific implementation method.

[0251] In another example, a second entity in a first federated learning group sends a registration request message to a second network element to request registration with the network. The registration request message carries the configuration parameters (profile) of the second entity. Accordingly, the second network element can save the configuration parameters of the second entity. The configuration parameters may include the identifier of the second candidate entity, the type of the second candidate entity, the federated learning tasks supported by the second candidate entity (e.g., the first federated learning task), the identifier of the first group to which the second candidate entity belongs, information about the vertical federated alliance to which the second candidate entity belongs, the first analysis identifier supported by the second candidate entity, the first interoperability identifier, or at least one of the identities supported by the second candidate entity in the first federated learning task. It should be understood that at least one second entity supports execution of the first federated learning task, wherein the at least one second entity includes at least one candidate entity, and the at least one candidate entity includes the first entity. Therefore, the second network element can obtain at least one candidate entity in the first federated learning group and its identity information in executing the first federated learning task, such as whether it supports providing tags in the first federated learning task or whether it has the ability to coordinate the first entity to execute the first federated learning task. For example, if the second entity includes AF1, AF2, NWDAF1, NWDAF2, and a third network element, AF1, AF2, NWDAF1, NWDAF2, and the third network element each send a registration request message to the second network element, carrying their respective configuration parameters. AF1 and AF2 support providing data in the first federated learning task, NWDAF1 and NWDAF2 support providing labels and data in the first federated learning task, and the third network element supports the coordination entity in executing the first federated learning task. Furthermore, the second network element may select some entities from the second entity as candidate entities, for example, at least one candidate entity includes AF1, AF2, NWDAF1, and the third network element.

[0252] S430: The second network element sends a third response message.

[0253] The third response message includes at least one candidate entity and identity information of at least one candidate entity. For example, the at least one candidate entity may include various types of entities, such as AF, NF, NWDAF, or a third network element (such as a task coordinator).

[0254] Optionally, in one implementation, in response to the above step S401 or S402, the second network element may send a third response message to the first network element or the third network element, see the following step S403 or S404 for details.

[0255] S403, the second network element sends a third response message to the first network element, and correspondingly, the first network element receives the third response message from the second network element.

[0256] For example, assuming that the first network element is AF1 and the second network element is NRF, after receiving the third request message from AF1, NRF can feedback NF instance and AF instance, such as AF2 and NF3, to AF1 according to the NF type and AF type carried in the third request message.

[0257] For example, assuming that the first network element is AF1 and the second network element is NEF, after receiving the third request message from AF1, NEF can determine the first federal training group or the first federal learning alliance based on the first group identifier or vertical federal alliance information carried in the third request message, and feedback NF instances and / or AF instances, such as NF1, AF2 and NF2, to AF1.

[0258] S404, the second network element sends a third response message to the third network element. Correspondingly, the third network element receives the third response message from the second network element.

[0259] For example, assuming that the third network element is the task coordinator and the second network element is the NRF, after receiving the third request message from the task coordinator, the NRF can determine the first federal training group or the first federal learning alliance based on the first group identifier or vertical federal alliance information carried in the third request message, and then can feedback the NF instances and / or AF instances in the first federal training group to the task coordinator, such as one or more of AF2, NF1, or NF3.

[0260] For example, assuming that the first network element is AF1 and the second network element is NEF, after receiving the third request message from AF1, NEF can feedback NF instance and AF instance, such as AF2 and NF3, to AF1 according to the NF type and AF type carried in the third request message.

[0261] Optionally, if the above steps S401 and S403 are not executed, or S403 is not executed, the third network element may send at least one candidate entity and identity information of at least one candidate entity to the first network element after executing step S404, see step S404a for details.

[0262] S404a, the third network element sends at least one candidate entity and identity information of at least one candidate entity to the first network element. Correspondingly, the first network element receives at least one candidate entity and identity information of at least one candidate entity from the third network element.

[0263] That is, the first network element may directly or indirectly obtain at least one candidate entity and identity information of at least one candidate entity from the second network element.

[0264] S440: The first network element determines the first entity and the first identity.

[0265] The first identity is used to indicate the identity of the first entity in executing the first federated learning task. It can be understood that the first identity is used to indicate the identity, role, or function of the first entity in executing the first federated learning task.

[0266] Optionally, this application does not limit the number of first entities, which can be one or more.

[0267] In one implementation, the first network element obtains at least one candidate entity and identity information of at least one candidate entity from the second network element based on step S403, and then selects or determines the first entity and the first identity from the at least one candidate entity and the identity information of the at least one candidate entity.

[0268] In another implementation, the first network element obtains at least one candidate entity and identity information of at least one candidate entity from a third network element, and then selects or determines the first entity and the first identity from the at least one candidate entity and the identity information of at least one candidate entity. This may be an internal implementation behavior of the first network element, or the first network element selects or determines the first entity and the first identity from the at least one candidate entity and the identity information of at least one candidate entity through predefined constraint rules. This application does not make any specific restrictions on this.

[0269] For example, assume that there are three candidate entities, AF1, AF2, and NF3, where AF1 supports providing labels in the first federated learning task, AF2 supports providing labels and data in the first federated learning task, and NF3 supports providing data in the first federated learning task. For example, the first network element can use AF1 as the master participant and AF2 and NF3 as slave participants, that is, the first entity includes AF1, AF2, and NF3, and the first identity of AF1 is used to indicate that AF1 provides labels in the first federated learning task, the first identity information of AF2 is used to indicate that AF2 provides data in the first federated learning task, and the first identity information of NF3 is used to indicate that NF3 provides data in the first federated learning task; for another example, the first network element can use AF2 as the master participant and NF3 as the slave participant, that is, the first entity includes AF2 and NF3, the first identity of AF2 is used to indicate that AF2 provides labels and data in the first federated learning task, and the first identity information of NF3 is used to indicate that NF3 provides data in the first federated learning task. That is to say, the first network element can determine all entities among the candidate entities as the first entities, or can determine most of the candidate entities as the first entities. This application does not limit this. Optionally, it depends on factors such as the current load and / or current capability information of the candidate entities.

[0270] Optionally, when the candidate entities include multiple entities capable of providing labels in the first federated learning task, the first network element may select one of the entities as the primary participant based on content logic or randomly. It should be understood that in the first federated learning task, there must be only one entity providing labels. In other words, entities participating in the first federated learning task can use the same label for federated learning training.

[0271] Optionally, in one implementation, before the first network element determines the first entity and the first identity, the first network element obtains a first analysis identifier, and the first analysis identifier corresponds to a first federated learning task; the first network element determines to trigger the first federated learning task based on the first analysis identifier.

[0272] Exemplarily, the first network element may obtain the first analysis identifier by: the first network element may receive a subscription request message from a consumer (e.g., an NWDAF including AnLF, or an NWDAF including MTLF), where the subscription request message includes the first analysis identifier, and the subscription request message is used to subscribe to federated learning model provisioning or training. The subscription request message may be a model subscription request message or an analysis subscription request message.

[0273] Exemplarily, the first analysis identifier can be predefined or preconfigured, where predefinition can include predefinition, such as protocol definition, and preconfiguration can be achieved by pre-saving corresponding codes, tables, strings or other methods for indicating the first analysis identifier in the first network element. This application does not limit its specific implementation method.

[0274] It should be understood that the first analysis identifier can be used to indicate a specific function or service associated with the model, that is, the model can be used to perform the specific function or service, or in other words, the model supports the execution of the specific function or service corresponding to the first analysis identifier. This specific function or service may be, for example, network performance analysis. The first analysis identifier corresponds to the first federated learning task, and it can be understood that the first analysis identifier is used to indicate the first federated learning task.

[0275] Optionally, the first network element may also obtain other information and, based on the correspondence between the other information and the first federated learning task, determine to trigger the first federated learning task. Exemplarily, the other information may include one or more of the following: a first group identifier, a first analysis identifier, a first interoperability identifier, a task identifier corresponding to the first federated learning task, or information about a vertical federated alliance. For specific explanations, please refer to the relevant description below.

[0276] Optionally, before executing step S440, that is, before the first network element determines the first entity and the first identity, the first network element may confirm with at least one candidate entity whether it agrees or supports the execution of the first federated learning task with the assigned identity. It should be understood that the at least one candidate entity includes the first entity. For ease of description, the following example uses the first network element confirming with the first entity whether it agrees or supports the execution of the first federated learning task with the assigned first identity or the first identity carried in the second request message. For details, see the following steps S411-S413 (not shown in the figure).

[0277] S411: The first network element sends a second request message to the first entity, and the first entity receives the second request message from the first network element, wherein the second request message is used to request the first entity to prepare to perform the first federated learning task.

[0278] Optionally, the second request message includes one or more of the following items. For details, refer to the above description:

[0279] (1) at least one identity supported by the first entity in the first federated learning task, where the at least one supported identity includes the first identity;

[0280] It should be understood that the identities supported by the first entity in the first federated learning task may be one or more, for example, including one or more of the following: providing labels, providing data, or coordinating the first entity to perform the first federated learning task.

[0281] (2) information about some or all entities that perform the first federated learning task, such as identification and / or identity information of some or all entities;

[0282] (3) first analysis identifier;

[0283] (4) The first group of logos;

[0284] (5) First identity;

[0285] (6) information of a third network element, where the third network element supports coordinating the first entity to perform the first federated learning task, such as a coordinator ID;

[0286] (7) Network identification, such as PLMN ID.

[0287] The network identifier is used to indicate a specific network in which the first network element requests the first entity to prepare to execute the first federated learning task.

[0288] (8) a task identifier corresponding to the first federated learning task, where the task identifier is used to identify the first federated learning task;

[0289] (9) Information on vertical federal alliances.

[0290] It should be noted that, in the embodiment of the present application, the identity supported by the first entity in the first federated learning task may be one or more. For example, it is assumed that the first entity supports multiple identities in the first federated learning task, that is, the first entity supports both providing labels and providing data in the first federated learning task. In comparison, the first identity represents the identity of the first entity in executing the first federated learning task, which may be that the first entity only provides labels in the first federated learning task, or, it may be that the first entity only provides data in the first federated learning task, or, it may be that the first entity provides labels and data in the first federated learning task. That is to say, the identity supported by the first entity in the first federated learning task may be a master participant and / or a slave participant, and the first identity may be a master participant or a slave participant, that is, the first identity belongs to the identity supported by the first entity in the first federated learning task.

[0291] Optionally, in one implementation, the first network element may send a fourth request message to the third network element. In response, the third network element receives the fourth request message from the first network element. The fourth request message is used to request the first entity to prepare to perform the first federated learning task. In other words, the first network element may directly or indirectly request the first entity to prepare to perform the first federated learning task. The parameters and their interpretations included in the fourth request message may refer to the description of the parameters and their interpretations included in the second request message and are not further described here.

[0292] S412: The first entity confirms whether it agrees to execute the first federated learning task.

[0293] Exemplarily, after verifying the identity of the first network element, the first entity can determine whether to agree to perform the first federated learning task, such as agree or disagree, based on its current load situation and / or whether it currently has the ability to perform the first federated learning task.

[0294] Optionally, if the second request message carries the first identity, the first entity determines whether it supports executing the first federated learning task with the first identity, or whether the first entity currently has the ability to execute the first federated learning task with the first identity, and then agrees or disagrees to execute the first federated learning task with the first identity.

[0295] Optionally, if the second request message carries the identity supported by the first entity in the first federated learning task, the first entity may determine whether to agree to perform the first federated learning task based on its current load situation and / or whether it currently has the ability to perform the first federated learning task. Alternatively, the first entity may also determine whether to agree to perform the first federated learning task with the supported identity based on its current load situation and / or whether it currently has the ability to perform the first federated learning task.

[0296] Optionally, if the first entity agrees to perform the first federated learning task, the following step S413 may be performed. It should be understood that the technical solution of the present application is performed on the basis that the first entity agrees to perform the first federated learning task.

[0297] Optionally, if the first entity does not agree to perform the first federated learning task, the first entity may send a failure reason value to the first network element to indicate a refusal to perform the first federated learning task, for example, the first entity refuses to perform the first federated learning task with the first identity, wherein the failure reason value may be that the current load of the first entity is too large, or the first entity currently does not support the first identity, or the first entity currently does not have the ability to perform the first federated learning task with the first identity.

[0298] S413: The first entity sends a second response message to the first network element, and the first network element receives the second response message from the first entity, wherein the second response message is used to indicate that the first entity agrees or supports executing the first federated learning task as the first identity.

[0299] Optionally, if the second request message carries the first identity, the first entity may carry the first identity in the second response message if it agrees to perform the first federated learning task, indicating that the first entity agrees or supports performing the first federated learning task with the first identity.

[0300] Optionally, if the second request message carries the identity supported by the first entity in the first federated learning task, the first entity may send the first identity to the first network element if it agrees to perform the first federated learning task, indicating that the first entity agrees or supports performing the first federated learning task with the first identity. It should be understood that the identities supported by the first entity in the first federated learning task include the first identity. Optionally, the second response message may carry the identity supported by the first entity in the first federated learning task, indicating that the first entity agrees to perform the first federated learning task with the identity or role assigned by the first network element, wherein the identity or role assigned by the first network element includes the first identity.

[0301] Optionally, if the second request message received by the first entity carries parameter (6) information of the third network element, the second response message can also be used to indicate that the first entity agrees that the third network element coordinates the first entity to perform the first federated learning task.

[0302] It should be noted that the above steps S411-S413 are described using the example of the first network element confirming with at least one candidate entity whether it agrees or supports the execution of the first federated learning task with the assigned identity. Optionally, before executing step S440, that is, before the first network element determines the first entity and the first identity, a third network element (e.g., a coordinator) may confirm with at least one candidate entity whether it agrees or supports the execution of the first federated learning task with the assigned identity. For details, see the following steps S414-S416 (not shown in the figure).

[0303] At step S414, the third network element sends an eighth request message to the at least one candidate entity, and in response, the at least one candidate entity receives the eighth request message from the third network element. The eighth request message is used to request the at least one candidate entity to perform the first federated learning task, and the eighth request message includes identity information of the at least one candidate entity.

[0304] Optionally, the eighth request message includes one or more of the following: For specific explanations, refer to the relevant description above:

[0305] (1) The identity supported by at least one candidate entity in the first federated learning task;

[0306] It should be understood that the identities supported by at least one candidate entity in the first federated learning task may be one or more, including one or more of the following: providing labels, providing data, or coordinating the first entity to perform the first federated learning task.

[0307] (2) information about some or all entities that perform the first federated learning task, such as identification and / or identity information of some or all entities;

[0308] (3) first analysis identifier;

[0309] (4) The first group of logos;

[0310] (5) First identity;

[0311] (6) information of a third network element, where the third network element supports coordinating the first entity to perform the first federated learning task, such as a coordinator ID;

[0312] (7) Network identification, such as PLMN ID.

[0313] (8) a task identifier corresponding to the first federated learning task, where the task identifier is used to identify the first federated learning task;

[0314] (9) Information on vertical federal alliances.

[0315] S415: At least one candidate entity confirms whether it agrees to execute the first federated learning task.

[0316] Exemplarily, after verifying the identity of the third network element, at least one candidate entity can determine whether to agree to perform the first federated learning task, such as agree or disagree, based on its current load situation and / or whether it currently has the ability to perform the first federated learning task.

[0317] Optionally, if the eighth request message carries the first identity, at least one candidate entity determines whether to support executing the first federated learning task with the first identity, or whether at least one candidate entity currently has the ability to execute the first federated learning task with the first identity, and then agrees or disagrees to execute the first federated learning task with the first identity.

[0318] Optionally, if the eighth request message carries an identity supported by at least one candidate entity in the first federated learning task, at least one candidate entity may determine whether to agree to perform the first federated learning task based on its current load situation and / or whether it currently has the ability to perform the first federated learning task, or at least one candidate entity may also determine whether to agree to perform the first federated learning task with the supported identity based on its current load situation and / or whether it currently has the ability to perform the first federated learning task.

[0319] Optionally, if at least one candidate entity agrees to perform the first federated learning task, the following step S416 may be performed. It should be understood that the technical solution of the present application is performed on the basis that at least one candidate entity agrees to perform the first federated learning task.

[0320] Optionally, if at least one candidate entity does not agree to perform the first federated learning task, the at least one candidate entity may send a failure reason value to the first network element to indicate a refusal to perform the first federated learning task, wherein the failure reason value may be that the at least one candidate entity is currently overloaded, or that the at least one candidate entity currently does not support the identity of the at least one candidate entity, or that the at least one candidate entity currently does not have the ability to perform the first federated learning task as the at least one candidate entity.

[0321] S416: The at least one candidate entity sends an eighth response message to the third network element, and the third network element receives the eighth response message from the at least one candidate entity. The eighth response message is used to indicate that the at least one candidate entity agrees to perform the first federated learning task as a candidate entity.

[0322] Optionally, if the eighth request message carries the first identity, at least one candidate entity may carry the first identity in the eighth response message if it agrees to perform the first federated learning task, indicating that at least one candidate entity agrees to support the execution of the first federated learning task with the first identity.

[0323] Optionally, if the eighth request message carries an identity supported by at least one candidate entity in the first federated learning task, then the at least one candidate entity, upon agreeing to perform the first federated learning task, may send the identity supported in the first federated learning task to the third network element, indicating that the at least one candidate entity agrees to support the performance of the first federated learning task with the first identity. It should be understood that the at least one candidate entity includes the first entity, and the identities supported by the first entity in the first federated learning task include the first identity. Optionally, the eighth response message may carry an identity supported by at least one candidate entity in the first federated learning task, indicating that the at least one candidate entity agrees to perform the first federated learning task with the assigned identity or role.

[0324] Optionally, if the eighth request message received by at least one candidate entity carries parameter (6) information of the third network element, the eighth response message can also be used to indicate that at least one candidate entity agrees that the third network element coordinates at least one candidate entity to perform the first federated learning task.

[0325] It should be noted that the above steps S411-S413 and steps S414-S416 can be executed selectively, or in other words, this application does not limit the execution subject of confirming whether at least one candidate entity agrees or supports executing the first federated learning task in an assigned capacity, such as the first network element, or the third network element, or other network elements with triggering capabilities.

[0326] Based on the above implementation, if the first entity agrees to execute the first federated learning task using the first identity, the first network element can finally determine the first entity and the first identity to execute the first federated learning task, thereby triggering the first entity to execute the first federated learning task. For details, see the following step S450. The specific execution process of the first federated learning task can be found in the description of method 300 above and will not be further described here.

[0327] S450: The first network element sends a first request message to the first entity. Correspondingly, the first entity receives the first request message from the first network element.

[0328] The first request message is used to request the first entity to perform the first federated learning task as the first identity. In other words, the first network element requests the first entity to perform the first federated learning task as the first identity. Exemplarily, the first identity includes a master participant or a slave participant. For a specific interpretation, refer to the description of step S420 above.

[0329] Optionally, the first network element may send a first request message directly to the first entity, or the first network element may first send a sixth request message to the third network element (e.g., a coordinator), where the sixth request message is used to request the creation or coordination of a first federated learning task. The sixth request message includes the first entity and the first identity. Correspondingly, after receiving the sixth request message, the third network element organizes the first entity to perform the first federated learning task with the first identity, and then the third network element may send a first federated learning task initialization request message to the first entity, requesting the first entity to perform the first federated learning task with the first identity.

[0330] Optionally, the sixth request message may further include one or more of the following items, for which specific interpretations can refer to the above related descriptions:

[0331] (1) First analysis identifier;

[0332] (2) The first group of logos;

[0333] (3) information about some or all entities that perform the first federated learning task, where the information about some or all entities includes identification and / or identity information;

[0334] (4) a task identifier corresponding to the first federated learning task, where the task identifier is used to identify the first federated learning task;

[0335] (5) Information on vertical federal alliances.

[0336] Optionally, the first request message may further include one or more of the following items, for which please refer to the above description for specific explanations:

[0337] (1) First analysis identifier;

[0338] (2) First identity;

[0339] (3) The first group of logos;

[0340] (4) information about some or all entities that perform the first federated learning task, where the information about some or all entities includes identification and / or identity information;

[0341] (5) Information about the third network element, such as the identifier of the third network element and / or the identity information of the third network element.

[0342] That is, the first network element may send the first identity to any first entity that performs the first federated learning task, and may also send the identifiers and / or identity information of other entities that perform the first federated learning task to the first entity.

[0343] (6) a task identifier corresponding to the first federated learning task, where the task identifier is used to identify the first federated learning task;

[0344] (7) Information on vertical federal alliances.

[0345] It should be noted that the above step S450 is explained by triggering the first network element (for example, the task initiator) to request the first entity to perform the first federated learning task as the first identity. Optionally, in one implementation, the third network element (for example, the coordinator) may also trigger the request to the first entity to perform the first federated learning task as the first identity. For details, see the following steps S405-S407.

[0346] S405: The third network element obtains the first entity and the first identity.

[0347] The first identity is used to indicate the identity of the first entity in executing the first federated learning task.

[0348] Optionally, in one implementation, the third network element may obtain the first entity and the first identity from the first network element, as described in detail in the following step S406.

[0349] S406 , the first network element sends the first entity and the first identity to the third network element. Correspondingly, the third network element receives the first entity and the first identity from the first network element.

[0350] Exemplarily, the first network element may send the first entity and the first identity to the third network element by carrying the first entity and the first identity in the sixth request message in the above step S450.

[0351] Optionally, in one implementation, the third network element may determine the first entity and the first identity on its own. For example, after obtaining at least one candidate entity and identity information of at least one candidate entity in step S404, the third network element further confirms whether the at least one candidate entity agrees to perform the first federated learning task by executing steps S414-S416 above, and finally determines the first entity and the first identity. The implementation method for the third network element to determine the first entity and the first identity may refer to the implementation method of the first network element in step S440 above, and is not specifically limited here.

[0352] Optionally, in one implementation, the third network element may obtain the first entity and the first identity from other network elements. The other network elements may be UDM, UDR, or a third-party server, etc. This application does not make any specific limitation on this.

[0353] Exemplarily, after obtaining the first entity and the first identity, the third network element may trigger the execution of the following step S407.

[0354] S407: The third network element sends a seventh request message to the first entity. Correspondingly, the first entity receives the seventh request message from the third network element.

[0355] The seventh request message is used to request the first entity to perform the first federated learning task, and the seventh request message includes the first identity.

[0356] Optionally, the first entity determines whether the sender of step S407, that is, the third network element, is consistent with the information of the third network element carried in the second request message in step S411. If they are the same, the first entity performs the first federated learning task as the first identity; if they are not the same, the first entity may refuse to perform the first federated learning task. In this implementation method, the first entity can avoid the first entity from performing other federated learning tasks initiated by a malicious third network element by determining whether the information of the third network element allocated by the first network element is the same as the information of the third network element that triggers the execution of the first federated learning task, thereby ensuring network security and reducing the processing load or signaling overhead of the first entity.

[0357] It should be noted that the above-mentioned step S450 and step S407 can be executed selectively, or in other words, this application does not limit the execution entity of the trigger requesting the first entity to perform the first federated learning task, such as the first network element, or the third network element, or other network elements with triggering capabilities.

[0358] Optionally, before executing step S405 or S406, the first network element may confirm whether the third network element agrees or supports coordinating the first entity to perform the first federated learning task, see the following steps S417-S418 for details (not shown in the figure).

[0359] S417: The first network element sends a fifth request message to the third network element, and the third network element receives the fifth request message from the first network element. The fifth request message is used to request the third network element to coordinate the first entity to perform the first federated learning task.

[0360] Optionally, the fifth request message may include one or more of the following: a first analysis identifier, a first group identifier, information about some or all entities that perform the first federated learning task, the information about some or all entities including identifier and / or identity information, information about a third network element, or a task identifier corresponding to the first federated learning task. For specific interpretations, please refer to the above-mentioned relevant descriptions.

[0361] S418: The third network element sends a fifth response message to the first network element, and the first network element receives the fifth response message from the third network element. The fifth response message is used to indicate that the third network element agrees or supports coordinating the first entity to perform the first federated learning task.

[0362] Optionally, if the third network element does not agree to coordinate the first entity to perform the first federated learning task, the third network element may send a failure reason value to the first network element to indicate a refusal to coordinate the first entity to perform the first federated learning task, wherein the failure reason value may be that the third network element is currently overloaded, or that the third network element currently does not support the identity of the coordinator, or that the third network element currently does not have the ability to coordinate the first entity to perform the first federated learning task.

[0363] It should be understood that this implementation is performed on the basis that the third network element agrees to coordinate the first entity to perform the first federated learning task.

[0364] According to the solution provided above, the first network element can ultimately determine the first entity and the first identity that executes the first federated learning task. By informing the first entity of its first identity in executing the first federated learning task, the first entity can execute the first federated learning task with the first identity, thereby effectively improving the execution efficiency of the first federated learning task.

[0365] FIG5 is a flow chart of a communication method 500 provided in an embodiment of the present application. As shown in FIG5 , the first network element (e.g., the task initiator) is NWDAF, the second network element (e.g., the entity discovery function network element) is NRF, and the task participants are AF1 and AF2 as the execution subjects for interaction. This method can be regarded as a further refinement of the above-mentioned method 400. It should be understood that the embodiment shown in FIG5 and the embodiment shown in FIG4 can be coupled with each other and can refer to each other. Therefore, the relevant description in the above-mentioned method 400 is also applicable to this implementation. The same or similar technical means may exist between the two. The content described in the embodiment shown in FIG4 will not be repeated. The method includes the following multiple steps. The part not fully described can refer to the above-mentioned method 400 or the existing protocol.

[0366] S501, the vertical federation participating entity initiates a registration process to the vertical federation entity discovery function network element. The specific registration process can refer to the relevant description of the above method 200 and will not be described here.

[0367] Optionally, in one implementation, the vertical federation participating entities include the NWDAF, AF1, and AF2. The federation entity discovery function network element can be an NRF or NEF. The NWDAF, AF1, and AF2 each send a registration request message to the NRF to request network registration. For ease of description, this implementation uses the NWDAF as the task initiator and AF1 and AF2 as task participants as an example.

[0368] The registration request message may carry configuration parameters (profile) of the vertical federation participating entity. The configuration parameters may include entity identification information, entity type, first analysis ID, first federated learning group ID to be joined (for example, the first federated learning group ID may be indicated by AF ID, PLMN ID, Vendor ID, or UE ID), or supported identities. For specific interpretations of the parameters, refer to the relevant description of the above method 400. Exemplarily, the first federated learning task includes one or more of model training, face recognition, or cross-institutional medical data analysis.

[0369] It should be noted that the embodiment of the present application does not limit the granularity level of the parameters carried in the registration request message.

[0370] S502 : NWDAF sends an entity discovery request message (ie, an example of the third request message) to NRF. Correspondingly, NRF receives the entity discovery request message from NWDAF.

[0371] The parameters and specific interpretations of the entity discovery request message may refer to the description of the third request message in step S401 of the above method 400 .

[0372] S503: The NRF determines at least one candidate entity and identity information of at least one candidate entity, such as AF1 and AF2, based on the first analysis ID and the first federated learning group ID. For specific implementation, please refer to the relevant description of step S420 of the above method 400.

[0373] Optionally, the NRF may also determine at least one candidate entity and identity information of at least one candidate entity based on other parameters carried in the entity discovery request message, such as information of the vertical federation alliance and / or the first interoperability identifier.

[0374] S504 , the NRF sends an entity discovery response message (ie, an example of the third response message) to the NWDAF. Correspondingly, the NWDAF receives the entity discovery response message from the NRF.

[0375] The parameters and specific interpretations of the entity discovery response message may refer to the description of the third response message in step S403 of the above method 400 .

[0376] Furthermore, the NWDAF may request at least one candidate entity (eg, AF1 and AF2) to prepare to perform the first federated learning task, as shown in the following steps S505-S510.

[0377] S505 , NWDAF sends a federated learning request task request message #1 (ie, an example of a second request message) to AF1 , and correspondingly, AF1 receives the federated learning request task request message #1 from NWDAF.

[0378] For example, NWDAF may send a federated learning request task request message #1 to AF1 through NEF.

[0379] Among them, the parameters and specific interpretations included in the federated learning request task request message #1 can refer to the relevant description of the second request message of the above method 400.

[0380] S506, AF1 agrees to participate in the federated learning task according to the assigned identity.

[0381] S507 , AF1 sends a federated learning request task response message #1 (ie, an example of a second response message) to NWDAF. Correspondingly, NWDAF receives the federated learning request task response message #1 from AF1 .

[0382] For example, AF1 may send a federated learning request task response message #1 to NWDAF through NEF.

[0383] Among them, the parameters and specific interpretations included in the federated learning request task request response #1 can be referred to the relevant description of the second response message of the above method 400.

[0384] S508, NWDAF sends a federated learning request task request message #2 (ie, an example of a second request message) to AF2. Correspondingly, AF2 receives the federated learning request task request message #2 from NWDAF.

[0385] Exemplarily, NWDAF may send a federated learning request task request message #2 to AF2 via NEF.

[0386] Among them, the parameters and specific interpretations included in the federated learning request task request message #2 can refer to the relevant description of the second request message of the above method 400.

[0387] S509, AF2 agrees to participate in the federated learning task according to the assigned identity.

[0388] S510, AF2 sends a federated learning request task response message #2 (ie, an example of a second response message) to NWDAF. Correspondingly, NWDAF receives the federated learning request task response message #2 from AF2.

[0389] For example, AF2 may send a federated learning request task response message #2 to NWDAF through NEF.

[0390] Among them, the parameters and specific interpretations included in the federated learning request task request response #2 can refer to the relevant description of the second response message of the above method 400.

[0391] It should be noted that the specific implementation of the above steps S505-S510 can refer to the relevant description of steps S411-S413 of the above method 400, and for the sake of brevity, they are not repeated here.

[0392] S511, NWDAF determines the first entity and the first identity.

[0393] Exemplarily, the NWDAF can determine the first entity and the first identity based on the feedback from steps S507 and S510. For example, the first entity includes AF1 and AF2. Accordingly, the first identity of AF1 can be a master participant, that is, AF1 provides labels in executing the first federated learning task, and the first identity of AF2 can be a slave participant, that is, AF2 provides data in executing the first federated learning task.

[0394] Exemplarily, NWDAF can determine the first entity and the first identity based on the feedback from steps S507 and S510. For example, the first entity includes AF1, AF2, and NWDAF. Accordingly, the first identity of NWDAF can be a master participant, that is, NWDAF provides labels in executing the first federated learning task. Optionally, NWDAF1 can also provide data in the first learning task. The first identities of AF1 and AF2 can be slave participants, that is, AF1 and AF2 provide data in executing the first federated learning task.

[0395] Optionally, NWDAF may or may not participate in the execution of the first federated learning task, which is not limited in this application. That is, the first entity executing the first federated learning task may include AF1, AF2, and NWDAF, or the first entity executing the first federated learning task may include AF1 and AF2.

[0396] Further, after determining the first entity and the first identity, the NWDAF may trigger a request to the first entity to perform the first federated learning task, which specifically includes the following steps S512-S514.

[0397] S512 , the NWDAF sends an initialization request message # 1 (ie, an example of a first request message) to AF1 , and AF1 receives the initialization request message # 1 from the NWDAF.

[0398] The parameters and specific definitions of the initialization request message #1 may be found in the description of the first request message of the above method 400.

[0399] S513 , AF1 sends an initialization response message # 1 to NWDAF. Correspondingly, NWDAF receives the initialization response message # 1 from AF1 .

[0400] Among them, the initialization response message #1 is used to instruct AF1 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0401] S514 , the NWDAF sends an initialization request message # 2 (ie, an example of the first request message) to AF2 , and correspondingly, AF2 receives the initialization request message # 2 from the NWDAF.

[0402] The parameters and specific definitions included in the initialization request message #2 may refer to the relevant description of the first request message of the above method 400.

[0403] S515 , AF2 sends an initialization response message # 2 to NWDAF. Correspondingly, NWDAF receives the initialization response message # 2 from AF2.

[0404] Among them, the initialization response message #2 is used to instruct AF2 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0405] Based on the above solution, NWDAF, as the task initiator, obtains at least one candidate entity and the identity information of at least one candidate entity, and then triggers confirmation from at least one candidate entity whether to agree to execute the first federated learning task, thereby ultimately determining the first entity and first identity to participate in executing the first federated learning task. This implementation method, in which the task initiator triggers the federated learning task initialization request and informs the first entity of its first identity in executing the first federated learning task, can effectively improve the execution efficiency of the first federated learning task.

[0406] It should be understood that Figure 5 above shows that the first federated learning task is initiated by NWDAF as the task initiator, that is, NWDAF determines the first entity and the first identity to perform the first federated learning task, and triggers the sending of a federated learning task initialization request to the first entity. Compared with Figure 5, Figure 6 shows that AF1 initiates the first federated learning task as the task initiator, and introduces a third network element (such as a coordinator). When the task initiator AF1 determines the first entity and the first identity to perform the first federated learning task, it triggers the request to the coordinator to organize or coordinate the first entity to complete the first federated learning task, that is, the coordinator triggers the sending of a federated learning task initialization request to the first entity.

[0407] FIG6 is a flow chart of a communication method 600 provided in an embodiment of the present application. As shown in FIG6 , the first network element (e.g., task initiator) is AF1, the second network element (e.g., entity discovery function network element) is NRF, the third network element is coordinator, and the task participants are NWDAF and AF2 as the execution subjects for interaction. This method can be regarded as a further refinement of the above-mentioned method 400. It should be understood that the embodiment shown in FIG6 and the embodiment shown in FIG4 can be coupled with each other and can be used as references to each other. Therefore, the relevant description in the above-mentioned method 400 is also applicable to this implementation method. The same or similar technical means may exist between the two. The content described in the embodiment shown in FIG4 will not be repeated. The method includes the following multiple steps. The part not fully described can refer to the above-mentioned method 400 or the existing protocol.

[0408] S601, NWDAF, AF1 and AF2 initiate a registration process to NRF. For the specific registration process, refer to the relevant description of the above method 200, and for the parameters carried in the registration process, refer to the relevant description of step S501 of the above method 500.

[0409] S602, AF1 sends an entity discovery request message (ie, an example of the third request message) to NRF, and correspondingly, NRF receives the entity discovery request message from AF1.

[0410] The parameters and specific interpretations of the entity discovery request message may refer to the description of the third request message in step S402 of the above method 400 .

[0411] S603: The NRF determines at least one candidate entity and identity information of at least one candidate entity, such as NWDAF and AF2, based on the first analysis ID and the first federated learning group ID. For specific implementation, please refer to the relevant description of step S420 of the above method 400.

[0412] Optionally, the NRF may also determine at least one candidate entity and identity information of at least one candidate entity based on other parameters carried in the entity discovery request message, such as information of the vertical federation alliance and / or the first interoperability identifier.

[0413] S604, NRF sends an entity discovery response message (ie, an example of the third response message) to AF1, and correspondingly, AF1 receives the entity discovery response message from NRF.

[0414] The parameters and specific interpretations of the entity discovery response message may refer to the description of the third response message in step S404 of the above method 400 .

[0415] S605 , AF1 determines at least one candidate entity and identity information of at least one candidate entity.

[0416] Furthermore, AF1 may request at least one candidate entity (eg, NWDAF and AF2) to prepare to perform the first federated learning task, as shown in the following steps S606-S613.

[0417] S606 , AF1 sends a federated learning task request message #1 (ie, an example of a second request message) to NWDAF. Correspondingly, NWDAF receives the federated learning task request message #1 from AF1 .

[0418] For example, AF1 may send a federated learning task request message #1 to NWDAF through NEF.

[0419] Among them, the parameters and specific interpretations included in the federated learning request task request message #1 can refer to the relevant description of the second request message of the above method 400.

[0420] S607, NWDAF agrees to participate in the federated learning task in accordance with the assigned identity.

[0421] S608, NWDAF agrees with the designated negotiator, that is, NWDAF agrees that the negotiator organizes or negotiates with NWDAF to perform the first federated learning task.

[0422] S609 , NWDAF sends a federated learning request task response message #1 (ie, an example of a second response message) to AF1 , and correspondingly, NWDAF receives the federated learning request task response message #1 from AF1 .

[0423] Exemplarily, NWDAF may send a federated learning request task response message #1 to AF1 through NEF.

[0424] Among them, the parameters and specific interpretations included in the federated learning request task request response #1 can be referred to the relevant description of the second response message of the above method 400.

[0425] S610, AF1 sends a federated learning request task request message #2 (ie, an example of a second request message) to AF2, and correspondingly, AF2 receives the federated learning request task request message #2 from AF1.

[0426] For example, AF1 may send a federated learning request task request message #2 to AF2 via NEF.

[0427] Among them, the parameters and specific interpretations included in the federated learning request task request message #2 can refer to the relevant description of the second request message of the above method 400.

[0428] S611, AF2 agrees to participate in the federated learning task according to the assigned identity.

[0429] S612, AF2 agrees with the designated negotiator, that is, AF2 agrees that the negotiator organizes or negotiates AF2 to perform the first federated learning task.

[0430] S613, AF2 sends a federated learning request task response message #2 (i.e., an example of a second response message) to AF1, and correspondingly, AF1 receives a federated learning request task response message #1 from AF2.

[0431] For example, AF2 may send a federated learning request task response message #1 to AF1 through NEF.

[0432] Among them, the parameters and specific interpretations included in the federated learning request task request response #2 can refer to the relevant description of the second response message of the above method 400.

[0433] It should be noted that the specific implementation of the above steps S606-S609 or steps S610-S613 can refer to the relevant description of steps S411-S413 of the above method 400, and for the sake of brevity, they are not repeated here.

[0434] S614, AF1 determines the first entity and the first identity.

[0435] For example, AF1 can determine the first entity and the first identity based on the feedback from steps S609 and S613. For example, the first entity includes NWDAF and AF2. Accordingly, NWDAF's first identity can be the master participant, that is, NWDAF provides labels and data in executing the first federated learning task, and AF2's first identity can be the slave participant, that is, AF2 provides data in executing the first federated learning task.

[0436] Optionally, AF1 may or may not participate in the first federated learning task, which is not limited in this application. That is, the first entity performing the first federated learning task may include AF1, AF2, and NWDAF, or the first entity performing the first federated learning task may include NWDAF and AF2.

[0437] Further, after determining the first entity and the first identity, AF1 may trigger a request to the negotiator to organize the first entity to perform the first federated learning task, which specifically includes the following steps S615-S622.

[0438] S615, AF1 sends a federated learning task creation request message (ie, an example of the sixth message) to the negotiator. Correspondingly, the negotiator receives the federated learning task creation request message from AF1.

[0439] Among them, the parameters and specific interpretations included in the federated learning task creation request message can be referred to the relevant description of the sixth request message of the above method 400.

[0440] S616 , the negotiator sends an initialization request message # 1 to the NWDAF. Correspondingly, the NWDAF receives the initialization request message # 1 from AF1 .

[0441] The parameters and specific definitions of the initialization request message #1 may be found in the description of the first request message of the above method 400.

[0442] S617: NWDAF verifies the negotiator ID.

[0443] Exemplarily, NWDAF compares whether the sender of step S616 is consistent with the negotiator determined in step S608. If they are the same, the following step S618 is executed. If they are not the same, NWDAF can refuse to execute the initialization request of step S616. In this implementation method, NWDAF can avoid NWDAF from executing federated learning tasks initiated by malicious negotiators by judging the negotiator ID assigned by the task initiator AF1 and the negotiator ID that triggers the federated learning task initialization request, thereby ensuring network security and reducing the processing load or signaling overhead of NWDAF.

[0444] S618, NWDAF sends an initialization response message #1 to AF1, and correspondingly, AF1 receives the initialization response message #1 from NWDAF.

[0445] Among them, the initialization response message #1 is used to instruct AF1 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0446] S619, the negotiator sends an initialization request message #2 to AF2, and correspondingly, AF2 receives the initialization request message #2 from the negotiator.

[0447] The parameters and specific definitions included in the initialization request message #2 may refer to the relevant description of the first request message of the above method 400.

[0448] S620, AF2 verifies the negotiator ID.

[0449] S621, AF2 sends an initialization response message #2 to AF1, and correspondingly, AF1 receives the initialization response message #2 from AF2.

[0450] Among them, the initialization response message #2 is used to instruct AF2 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0451] For the specific implementation of steps S619-S621, please refer to the relevant description of steps S616-S618.

[0452] S622, the negotiator sends a federated learning task creation response message to AF1, and correspondingly, AF1 receives the federated learning task creation response message from the negotiator.

[0453] The federated learning task creation response message is used to indicate that the negotiator has organized or negotiated NWDAF and AF2 to execute or complete the first federated learning task.

[0454] Based on the above solution, AF1, as the task initiator, obtains at least one candidate entity and the identity information of at least one candidate entity, and triggers confirmation from at least one candidate entity whether it agrees to execute the first federated learning task, thereby ultimately determining the first entity and first identity participating in the execution of the first federated learning task. This implementation method, in which the negotiator creates or coordinates the first federated learning task and triggers an initialization request for the first federated learning task, effectively improves the execution efficiency of the first federated learning task by informing the first entity of its first identity in executing the first federated learning task.

[0455] It should be understood that Figure 6 above shows that AF1 initiates the first federated learning task as the task initiator, and AF1 confirms with at least one candidate entity whether it agrees to participate in the federated learning task with the assigned identity. When NWDAF determines the first entity and the first identity to perform the first federated learning task, it triggers a request to the third network element (such as a coordinator) to organize or coordinate the first entity to complete the first federated learning task, that is, the coordinator triggers the sending of a federated learning task initialization request to the first entity. Compared with Figure 6, Figure 7 shows that AF1 initiates the first federated learning task as the task initiator, and then introduces a third network element (such as a coordinator). The coordinator confirms with at least one candidate entity whether it agrees to participate in the federated learning task with the assigned identity. When NWDAF determines the first entity and the first identity to perform the first federated learning task, the coordinator triggers the sending of a federated learning task initialization request to the first entity.

[0456] FIG7 is a flow chart of a communication method 700 provided in an embodiment of the present application. As shown in FIG7 , the first network element is the initiator, the second network element is the NRF, and the third network element is the coordinator as the execution subjects to interact. This method can be regarded as a further refinement of the above-mentioned method 400. It should be understood that the embodiment shown in FIG7 and the embodiment shown in FIG4 can be coupled with each other and can refer to each other. Therefore, the relevant description in the above-mentioned method 400 is also applicable to this implementation mode. The same or similar technical means may exist between the two. The content described in the embodiment shown in FIG4 will not be repeated. The method includes the following multiple steps. For the part not fully described, reference can be made to the above-mentioned method 400 or the existing protocol.

[0457] S701, NWDAF, AF1 and AF2 initiate a registration process to NRF. For the specific registration process, refer to the relevant description of the above method 200, and for the parameters carried in the registration process, refer to the relevant description of step S601 of the above method 600.

[0458] S702, AF1 sends a federated learning task request message #1 to the coordinator. Correspondingly, the coordinator receives the federated learning task request message #1 from AF1.

[0459] For example, AF1 may send a federated learning task request message #1 to the coordinator via NEF.

[0460] Among them, the parameters and specific interpretations included in the federated learning request task request message #1 can refer to the relevant description of the second request message of the above method 400.

[0461] Furthermore, the coordinator triggers the acquisition of at least one candidate entity for executing the federated learning task and the identity information of at least one candidate entity. For example, the coordinator may pre-configure or configure the acquisition of the candidate entity for executing the federated learning task through signaling; or the coordinator may request the NRF to acquire the candidate entity for executing the federated learning task, specifically including the following steps S703-S705.

[0462] S703 , the coordinator sends an entity discovery request message (ie, an example of the third request message) to the NRF. Correspondingly, the NRF receives the entity discovery request message from the coordinator.

[0463] The parameters and specific interpretations of the entity discovery request message may refer to the description of the third request message in step S402 of the above method 400 .

[0464] S704: The NRF determines at least one candidate entity and identity information of at least one candidate entity, such as NWDAF and AF2, based on the first analysis ID and the first federated learning group ID. For specific implementation, please refer to the relevant description of step S420 of the above method 400.

[0465] Optionally, the NRF may also determine at least one candidate entity and identity information of at least one candidate entity based on other parameters carried in the entity discovery request message, such as information of the vertical federation alliance and / or the first interoperability identifier.

[0466] S705 , the NRF sends an entity discovery response message (ie, an example of the third response message) to the coordinator. Correspondingly, the coordinator receives the entity discovery response message from the NRF.

[0467] The parameters and specific interpretations of the entity discovery response message may refer to the description of the third response message in step S404 of the above method 400 .

[0468] Furthermore, after determining at least one candidate entity (e.g., NWDAF and AF2) and the identity information of at least one candidate entity, the coordinator may confirm with the at least one candidate entity whether it is willing or agrees to participate in the execution of the first federated learning task with the assigned identity, specifically including the following steps S706-S713.

[0469] S706 , the coordinator sends a federated learning task request message # 2 (ie, an example of the eighth request message) to the NWDAF. Correspondingly, the NWDAF receives the federated learning task request message # 2 from the coordinator.

[0470] For example, the coordinator may send a federated learning task request message #2 to the NWDAF via the NEF.

[0471] Among them, the parameters and specific interpretations included in the federated learning request task request message #2 can refer to the relevant description of the eighth request message of the above method 400.

[0472] S707, NWDAF agrees to participate in the federated learning task in accordance with the assigned identity.

[0473] S708, NWDAF agrees with the designated negotiator, that is, NWDAF agrees that the negotiator organizes or negotiates with NWDAF to perform the first federated learning task.

[0474] S709 , the NWDAF sends a federated learning request task response message #2 (ie, an example of the eighth response message) to the coordinator. Correspondingly, the NWDAF receives the federated learning request task response message #2 from the coordinator.

[0475] Exemplarily, the NWDAF may send a federated learning request task response message #2 to the coordinator through the NEF.

[0476] Among them, the parameters and specific interpretations included in the federated learning request task request response #2 can be referred to the relevant description of the eighth response message of the above method 400.

[0477] S710 , the coordinator sends a federated learning request task request message #3 (ie, an example of the eighth request message) to AF2 , and correspondingly, AF2 receives the federated learning request task request message #3 from the coordinator.

[0478] For example, the coordinator may send a federated learning request task request message #3 to AF2 via NEF.

[0479] Among them, the parameters and specific interpretations included in the federated learning request task request message #3 can be referred to the relevant description of the eighth request message of the above method 400.

[0480] S711, AF2 agrees to participate in the federated learning task according to the assigned identity.

[0481] S712, AF2 agrees with the designated negotiator, that is, AF2 agrees that the negotiator organizes or negotiates AF2 to perform the first federated learning task.

[0482] S713, AF2 sends a federated learning request task response message #3 (i.e., an example of the eighth response message) to AF1, and correspondingly, AF1 receives the federated learning request task response message #3 from AF2.

[0483] For example, AF2 may send a federated learning request task response message #3 to the coordinator via NEF.

[0484] Among them, the parameters and specific interpretations included in the federated learning request task request response #3 can be referred to the relevant description of the eighth response message of the above method 400.

[0485] It should be noted that the specific implementation of the above steps S706-S709 or steps S710-S713 can refer to the relevant description of steps S411-S413 of the above method 400, and for the sake of brevity, they are not repeated here.

[0486] S714: The negotiator determines at least one candidate entity and identity information of at least one candidate entity.

[0487] Exemplarily, the negotiator may determine at least one candidate entity and identity information of at least one candidate entity based on the feedback from steps S709 and S713. It should be noted that the at least one candidate entity and identity information of at least one candidate entity determined in step S714 may be exactly the same as the identity information of at least one candidate entity and at least one candidate entity obtained in step S705, or may be different. For example, the identity information of at least one candidate entity and at least one candidate entity determined in step S714 is included in the identity information of at least one candidate entity and at least one candidate entity obtained in step S705, and this application does not limit this.

[0488] In step S715, the negotiator sends a federated learning task response message #1 to AF1. In response, AF1 receives the federated learning task response message #1 from the negotiator. The federated learning task response message #1 includes the at least one candidate entity determined in step S714 and the identity information of the at least one candidate entity.

[0489] S716, AF1 determines the first entity and the first identity.

[0490] It should be understood that the first entity belongs to at least one candidate entity. For example, AF1 determines the first entity and the first identity from at least one candidate entity and identity information of at least one candidate entity.

[0491] Exemplarily, if at least one candidate entity includes NWDAF and AF2, wherein NWDAF is capable of providing labels and data in performing the first federated learning task and AF2 is capable of providing data in performing the first federated learning task, then AF1 can determine that the first entity includes NWDAF and AF2, wherein the first identity of NWDAF can be a master participant, such as NWDAF provides labels and data in performing the first federated learning task, and the first identity of AF2 can be a slave participant, such as AF2 provides data in performing the first federated learning task.

[0492] Exemplarily, if at least one candidate entity includes NWDAF and AF2, where both NWDAF and AF2 can provide labels and data in performing the first federated learning task, then AF1 can determine that the first entity includes NWDAF and AF2, where the first identity of NWDAF can be a master participant, providing labels and data in performing the first federated learning task, and the first identity of AF2 can be a slave participant, providing data in performing the first federated learning task.

[0493] Optionally, AF1 may participate in executing the first federated learning task or may not participate in executing the first federated learning task, which is not limited in this application.

[0494] Optionally, after determining the first entity and the first identity, AF1 may finally notify the negotiator of the first entity and the first identity participating in executing the first federated learning task. For details, see the following step S716.

[0495] S716, AF1 sends a first message to the negotiator, and correspondingly, the negotiator receives the first message from AF1.

[0496] The first message indicates the first entity and the first identity. It should be noted that if the first entity and the first identity finally determined by AF1 are completely consistent with the candidate entity and the identity information of the candidate entity sent by the negotiator in step S715, AF1 may not execute step S716. If the first entity and the first identity finally determined by AF1 are inconsistent with the candidate entity and the identity information of the candidate entity sent by the negotiator in step S715, AF1 needs to execute step S716 to notify the first entity and the first identity that finally execute the first federated learning task.

[0497] Furthermore, after the negotiator obtains the first entity and the first identity, the negotiator may trigger a request to the first entity to perform the first federated learning task, which specifically includes the following steps S717-S720.

[0498] S717 , the negotiator sends an initialization request message # 1 to the NWDAF. Correspondingly, the NWDAF receives the initialization request message # 1 from AF1 .

[0499] The parameters and specific definitions of the initialization request message #1 may be found in the description of the first request message of the above method 400.

[0500] S718 , NWDAF sends an initialization response message # 1 to AF1 , and correspondingly, AF1 receives the initialization response message # 1 from NWDAF.

[0501] Among them, the initialization response message #1 is used to instruct AF1 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0502] S719 , the negotiator sends an initialization request message # 2 to AF2 , and correspondingly, AF2 receives the initialization request message # 2 from the negotiator.

[0503] The parameters and specific definitions included in the initialization request message #2 may refer to the relevant description of the first request message of the above method 400.

[0504] S720, AF2 sends an initialization response message #2 to AF1, and correspondingly, AF1 receives the initialization response message #2 from AF2.

[0505] Among them, the initialization response message #2 is used to instruct AF2 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0506] Based on the above scheme, AF1, as the task initiator, triggers the negotiator to obtain at least one candidate entity and the identity information of at least one candidate entity. The negotiator then confirms with the at least one candidate entity whether it agrees to execute the first federated learning task. Ultimately, AF1 determines the first entity and the first identity that will participate in executing the first federated learning task from the at least one candidate entity and the identity information of the at least one candidate entity. This implementation method involves the negotiator obtaining the identity information of at least one candidate entity and at least one candidate entity, and triggering a federated learning task initialization request. By informing the first entity of its first identity in executing the first federated learning task, the execution efficiency of the first federated learning task can be effectively improved.

[0507] It should be understood that Figure 5 shows the NWDAF initiating the first federated learning task as the task initiator. That is, the NWDAF determines the first entity and first identity to execute the first federated learning task, and triggers the sending of a federated learning task initialization request to the first entity. Compared to Figure 5, Figure 8 shows the NWDAF initiating the first federated learning task as the task initiator, while introducing a third network element (such as a coordinator) to organize or coordinate the first entity to complete the first federated learning task.

[0508] FIG8 is a flow chart of a communication method 800 provided in an embodiment of the present application. As shown in FIG8 , the first network element is the initiator, the second network element is the NRF, and the third network element is the coordinator as the execution subjects to interact. This method can be regarded as a further refinement of the above-mentioned method 400. It should be understood that the embodiment shown in FIG8 and the embodiment shown in FIG4 can be coupled with each other and can refer to each other. Therefore, the relevant description in the above-mentioned method 400 is also applicable to this implementation mode. The same or similar technical means may exist between the two. The content described in the embodiment shown in FIG4 will not be repeated. The method includes the following multiple steps. For the part not fully described, reference can be made to the above-mentioned method 400 or the existing protocol.

[0509] S801, NWDAF, AF1 and AF2 initiate a registration process to NRF.

[0510] S802 , the NWDAF sends an entity discovery request message (ie, an example of the third request message) to the NRF. Correspondingly, the NRF receives the entity discovery request message from the NWDAF.

[0511] S803: The NRF determines at least one candidate entity and identity information of at least one candidate entity, such as AF1 and AF2, according to the first analysis ID and the first federated learning group ID.

[0512] Optionally, the NRF may also determine at least one candidate entity and identity information of at least one candidate entity based on other parameters carried in the entity discovery request message, such as information of the vertical federation alliance and / or the first interoperability identifier.

[0513] S804 , the NRF sends an entity discovery response message (ie, an example of the third response message) to the NWDAF. Correspondingly, the NWDAF receives the entity discovery response message from the NRF.

[0514] The specific implementation of the above steps S801-S804 can refer to the relevant description of the above steps S501-S504.

[0515] In this implementation, a coordinator (i.e., an example of a third network element) is introduced to organize or coordinate the first entity to execute the first federated learning task, thereby orderly improving the execution efficiency of the first federated learning task. Therefore, before executing the first federated learning task, the NWDAF can confirm with the coordinator whether it agrees or is willing to coordinate the first entity to execute the first federated learning task. For details, see steps S805-S806 below.

[0516] S805 , the NWDAF sends a coordination request message (ie, an example of the fifth request message) to the coordinator. Correspondingly, the coordinator receives the coordination request message from the NWDAF.

[0517] The parameters and specific interpretations of the coordination request message may refer to the relevant description of the fifth request message of the above method 400.

[0518] S806 , the coordinator sends a coordination response message (ie, an example of the fifth response message) to the NWDAF. Correspondingly, the NWDAF receives the coordination response message from the coordinator.

[0519] The parameters and specific interpretations of the coordination request message may refer to the relevant description of the fifth response message of the above method 400.

[0520] Furthermore, the NWDAF may request at least one candidate entity (eg, AF1 and AF2) to prepare to perform the first federated learning task, as shown in the following steps S807-S814.

[0521] S807 , NWDAF sends a federated learning request task request message #1 (ie, an example of a second request message) to AF1 , and correspondingly, AF1 receives the federated learning request task request message #1 from NWDAF.

[0522] For example, NWDAF may send a federated learning request task request message #1 to AF1 through NEF.

[0523] Among them, the parameters and specific interpretations included in the federated learning request task request message #1 can refer to the relevant description of the second request message of the above method 400.

[0524] S808, AF1 agrees to participate in the federated learning task according to the assigned identity.

[0525] S809, AF1 agrees with the designated negotiator, that is, AF1 agrees that the negotiator organizes or negotiates AF1 to perform the first federated learning task.

[0526] S810, AF1 sends a federated learning request task response message #1 (ie, an example of a second response message) to NWDAF. Correspondingly, NWDAF receives the federated learning request task response message #1 from AF1.

[0527] For example, AF1 may send a federated learning request task response message #1 to NWDAF through NEF.

[0528] Among them, the parameters and specific interpretations included in the federated learning request task request response #1 can be referred to the relevant description of the second response message of the above method 400.

[0529] S811, NWDAF sends a federated learning request task request message #2 (ie, an example of a second request message) to AF2, and correspondingly, AF2 receives the federated learning request task request message #2 from NWDAF.

[0530] Exemplarily, NWDAF may send a federated learning request task request message #2 to AF2 via NEF.

[0531] Among them, the parameters and specific interpretations included in the federated learning request task request message #2 can refer to the relevant description of the second request message of the above method 400.

[0532] S812, AF2 agrees to participate in the federated learning task according to the assigned identity.

[0533] S813, AF2 agrees to designate a negotiator, that is, AF2 agrees that the negotiator will organize or negotiate with AF2 to perform the first federated learning task.

[0534] S814, AF2 sends a federated learning request task response message #2 (i.e., an example of a second response message) to NWDAF. Correspondingly, NWDAF receives the federated learning request task response message #1 from AF2.

[0535] For example, AF2 may send a federated learning request task response message #2 to NWDAF through NEF.

[0536] Among them, the parameters and specific interpretations included in the federated learning request task request response #2 can refer to the relevant description of the second response message of the above method 400.

[0537] It should be noted that the specific implementation of the above steps S807-S814 can refer to the relevant description of steps S411-S413 of the above method 400, and for the sake of brevity, they are not repeated here.

[0538] S815: NWDAF determines the first entity and the first identity.

[0539] Further, after determining the first entity and the first identity, the NWDAF may trigger a request to the first entity to perform the first federated learning task, which specifically includes the following steps S816-S820.

[0540] S816 , the NWDAF sends the first entity and the first identity to the coordinator, and correspondingly, the coordinator receives the first entity and the first identity from the NWDAF.

[0541] S817 , the NWDAF sends an initialization request message # 1 (ie, an example of a first request message) to AF1 , and AF1 receives the initialization request message # 1 from the NWDAF.

[0542] The parameters and specific definitions of the initialization request message #1 may be found in the description of the first request message of the above method 400.

[0543] S818, AF1 sends an initialization response message #1 to NWDAF, and correspondingly, NWDAF receives the initialization response message #1 from AF1.

[0544] Among them, the initialization response message #1 is used to instruct AF1 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0545] S819 , the NWDAF sends an initialization request message #2 (ie, an example of the first request message) to AF2 , and correspondingly, AF2 receives the initialization request message #2 from the NWDAF.

[0546] The parameters and specific definitions included in the initialization request message #2 may refer to the relevant description of the first request message of the above method 400.

[0547] S820, AF2 sends an initialization response message #2 to NWDAF, and correspondingly, NWDAF receives the initialization response message #2 from AF2.

[0548] Among them, the initialization response message #2 is used to instruct AF2 to confirm to perform the first federated learning task with the assigned identity next. For the specific federated learning process, please refer to the relevant description of the above method 300.

[0549] Based on the above solution, NWDAF, as the task initiator, obtains at least one candidate entity and the identity information of at least one candidate entity, and triggers confirmation from the coordinator and at least one candidate entity whether they agree to execute the first federated learning task, thereby ultimately determining the first entity and the first identity that will participate in executing the first federated learning task. This implementation method, in which NWDAF triggers a federated learning task initialization request, notifies the first entity of its first identity in executing the first federated learning task, and the coordinator organizes or coordinates the first entity to execute the first federated learning task, can effectively improve the execution efficiency of the first federated learning task.

[0550] The communication method embodiment of the present application is described above in conjunction with Figures 1 to 8 . The communication device embodiment of the present application will be described in detail below in conjunction with Figures 9 and 10 . It should be understood that the description of the device embodiment corresponds to the description of the method embodiment. Therefore, for portions not described in detail, reference can be made to the aforementioned method embodiment.

[0551] FIG9 is a schematic diagram of a communication device 1000 provided in an embodiment of the present application. As shown in FIG9 , the communication device 1000 includes a communication module 1002 and a processing module 1001. The communication device 1000 can be a first network element, or a communication device applied to the first network element or used in conjunction with the first network element and capable of implementing the method executed by the first network element, such as a chip, a chip system, or a circuit; or the communication device 1000 can be a second network element, or a communication device applied to the second network element or used in conjunction with the second network element and capable of implementing the method executed by the second network element, such as a chip, a chip system, or a circuit; or the communication device 1000 can be a third network element, or a communication device applied to the third network element or used in conjunction with the third network element and capable of implementing the method executed by the third network element, such as a chip, a chip system, or a circuit.

[0552] The communication module 1002 may also be referred to as a transceiver module, transceiver, transceiver, or transceiver device. The processing module 1001 may also be referred to as a processor, processing board, processing unit, or processing device. Optionally, the communication module 1002 is configured to perform the sending and receiving operations of the first network element, the second network element, or the third network element in the above method. The device in the communication module 1002 that implements the receiving function may be considered a receiving unit, and the device in the communication module 1002 that implements the sending function may be considered a sending unit. That is, the communication module 1002 includes a receiving unit and a sending unit.

[0553] When the communication device 1000 is applied to the first network element, the processing module 1001 can be used to implement the processing function of the first network element in the above embodiments, and the communication module 1002 can be used to implement the transceiver function of the first network element in the above embodiments.

[0554] When the communication device 1000 is applied to the second network element, the processing module 1001 can be used to implement the processing function of the second network element in the above embodiments, and the communication module 1002 can be used to implement the transceiver function of the second network element in the above embodiments.

[0555] When the communication device 1000 is applied to a third network element, the processing module 1001 may be used to implement the processing function of the third network element in the above embodiments, and the communication module 1002 may be used to implement the transceiver function of the third network element in the above embodiments.

[0556] In addition, it should be noted that the aforementioned communication module and / or processing module can be implemented by a virtual module, for example, the processing module can be implemented by a software functional unit or a virtual device, and the communication module can be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented by a physical device, for example, if the device is implemented using a chip / circuit (such as an integrated circuit or a logic circuit, etc.). The communication module can be an input / output circuit and / or a communication interface that performs input operations (corresponding to the aforementioned receiving operations) and output operations (corresponding to the aforementioned sending operations); the processing module is an integrated processor or microprocessor or circuit (such as an integrated circuit or a logic circuit, etc.).

[0557] The division of modules in this application is illustrative and represents only a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the examples of this application may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules.

[0558] FIG10 is a schematic diagram of another communication device 2000 provided in an embodiment of the present application. As shown in FIG10 , the communication device 2000 may optionally be a chip or a chip system. Optionally, in the present application, the chip system may be composed of a chip or may include a chip and other discrete devices.

[0559] The communication device 2000 can be used to implement the functions of any network element (e.g., the first network element, the second network element, or the third network element) in the communication system described in the above example. The communication device 2000 may include a communication interface 2030 and a processor 2010. The communication device 2000 can exchange information with other devices through the communication interface 2030. Exemplarily, the communication interface 2030 can be a transceiver, a circuit, a bus, a module, a pin, or other types of communication interfaces. When the communication device 2000 is a chip-type device or circuit, the communication interface 2030 in the device 2000 can also be an input-output circuit that can input information (or receive information) and output information (or send information). The processor 2010 is an integrated processor, microprocessor, integrated circuit, or logic circuit, etc. The processor can determine output information based on the input information.

[0560] Optionally, the processor 2010 is coupled to a memory, which may be located within the device, integrated with the processor, or external to the device. For example, the communication device 2000 may further include at least one memory 2020. The memory 2020 stores the necessary computer programs, computer programs, instructions, and / or data for implementing any of the above examples. The processor 2010 may execute the computer program stored in the memory 2020 to perform the method in any of the above examples.

[0561] Coupling in this application refers to an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information exchange between devices, units, or modules. The processor 2010 may operate in conjunction with the memory 2020 and the communication interface 2030. The specific connection medium between the processor 2010, memory 2020, and communication interface 2030 is not limited in this application.

[0562] Optionally, as shown in FIG10 , the processor 2010, the memory 2020, and the communication interface 2030 are interconnected via a bus 2040. Optionally, the bus may include an address bus, a data bus, a control bus, and other types of buses. Furthermore, for ease of illustration, FIG10 shows one bus 2040, but this does not mean that there is only one bus or only one type of bus.

[0563] It should be understood that the processors mentioned in the embodiments of the present application may be the following devices or the circuit portions of the following devices used for processing functions: a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0564] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory and / or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM). For example, RAM can be used as an external cache. By way of example and not limitation, RAM includes the following forms: static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0565] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) can be integrated into the processor.

[0566] It should also be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0567] An embodiment of the present application also provides a computer-readable storage medium on which computer instructions are stored for implementing the method executed by at least one of the first network element, the second network element, or the third network element in the above-mentioned method embodiments.

[0568] An embodiment of the present application also provides a computer program product comprising instructions, which, when executed by a computer, implement the method performed by at least one of the first network element, the second network element, or the third network element in the above-mentioned method embodiments.

[0569] An embodiment of the present application further provides a communication system, which includes at least one of the first network element, the second network element, or the third network element in the above embodiments.

[0570] The explanation of the relevant contents and beneficial effects of any of the above-mentioned devices can be referred to the corresponding method embodiments provided above and will not be described again here.

[0571] To facilitate understanding of the above embodiments provided in this application, the following points are explained:

[0572] 1) In this application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0573] 2) In this application, "at least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In the text description of this application, the character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b and c can mean: a, or b, or c, or a and b, or a and c, or b and c, or a, b and c. Where a, b and c can be single or multiple, respectively.

[0574] 3) Throughout this application, the terms "first," "second," and various numerical references (e.g., #1, #2, etc.) are used to distinguish between different messages for ease of description and are not intended to limit the scope of the embodiments of this application. For example, they are used to distinguish between different messages, rather than to describe a specific order or precedence. It should be understood that such references are interchangeable, where appropriate, to allow for the description of scenarios beyond the embodiments of this application.

[0575] 4) In this application, descriptions such as "when...", "in the case of...", and "if" all mean that the device will perform corresponding processing under certain objective circumstances. They do not limit the time, nor do they require the device to perform judgment actions when implementing them, nor do they mean that there are other limitations.

[0576] 5) In this application, "used to indicate" can include being used for direct indication and being used for indirect indication. When describing that a certain indication information is used to indicate A, it can include that the indication information directly indicates A or indirectly indicates A, and it does not mean that the indication information must carry A.

[0577] The indication methods involved in the embodiments of this application should be understood to encompass various methods that enable the party to be indicated to obtain information about the information to be indicated. The information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately. The transmission period and / or timing of these sub-information can be the same or different. This application does not limit the transmission method, for example.

[0578] In the embodiments of the present application, the "indication information" may be an explicit indication, i.e., a direct indication via signaling, or may be obtained based on parameters indicated by the signaling, in combination with other rules, other parameters, or by deduction. It may also be an implicit indication, i.e., based on a rule or relationship, or based on other parameters, or by deduction. This application does not impose specific limitations on this.

[0579] 6) In this application, "protocol" may refer to a standard protocol in the field of communications, such as 5G protocol, NR protocol, and related protocols used in future communication systems, which is not limited in this application. "Predefined" may include pre-definition. For example, protocol definition. "Preconfiguration" can be implemented by pre-saving corresponding codes, tables, or other methods that can be used to indicate relevant information in the device, and this application does not limit its implementation method.

[0580] 7) In this application, "communication" may also be described as "data transmission", "information transmission", "data processing", etc. "Transmission" includes "sending" and "receiving".

[0581] In this application, configuration may refer to signaling configuration, or may be described as configuration signaling. For example, signaling configuration may be configured by a network device sending signaling, which may be a radio resource control (RRC) message, downlink control information (DCI), or system information block (SIB). For another example, signaling configuration may be pre-configured, where pre-configuration is to define or configure the values ​​of corresponding parameters in advance in a protocol manner, which may be stored in the device during communication, and this application does not limit this.

[0582] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0583] In this application, under the premise of no logical contradiction, the examples can reference each other, for example, the methods and / or terms between method embodiments can reference each other, for example, the functions and / or terms between device embodiments can reference each other, for example, the functions and / or terms between device examples and method examples can reference each other.

[0584] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0585] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be described again here.

[0586] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0587] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0588] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0589] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0590] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that, Including: A first network element determines a first entity and a first identity, where the first identity is used to indicate the identity of the first entity in performing a first federated learning task; The first network element sends a first request message to the first entity, where the first request message is used to request the first entity to perform the first federated learning task with the first identity.

2. The method according to claim 1, wherein The first identity includes a main participant or a subordinate participant, where, The main participant supports providing labels in the first federated learning task, and the labels correspond to the first federated learning task; or, the main participant supports providing labels and data in the first federated learning task; The subordinate participant supports providing data in the first federated learning task.

3. The method according to claim 1 or 2, characterized in that, The method further includes: The first network element obtains at least one candidate entity and identity information of the at least one candidate entity, where the identity information of the at least one candidate entity is used to indicate the identity supported by the at least one candidate entity in the first federated learning task; The first network element determines the first entity and the first identity, including: The first network element determines the first entity and the first identity from the at least one candidate entity and the identity information of the at least one candidate entity.

4. The method according to claim 3, characterized in that, The first network element obtains at least one candidate entity and identity information of the at least one candidate entity, including: The first network element receives the at least one candidate entity and the identity information of the at least one candidate entity from a second network element, and the second network element supports discovering entities performing the first federated learning task.

5. The method according to claim 4, characterized in that The first network element receives the at least one candidate entity and the identity information of the at least one candidate entity from the second network element, including: The first network element sends a third request message to the second network element, where the third request message is used to obtain entities supporting the execution of the first federated learning task; The first network element receives a third response message from the second network element, and the third response message includes the at least one candidate entity and the identity information of the at least one candidate entity.

6. The method according to any one of claims 1 to 5, characterized in that Before the first network element determines the first entity and the first identity, the method further includes: The first network element sends a second request message to the first entity, where the second request message is used to request the first entity to prepare for performing the first federated learning task; The first network element receives a second response message from the first entity, and the second response message is used to indicate that the first entity agrees to perform the first federated learning task with the first identity.

7. The method according to claim 6, wherein The second request message includes at least one identity supported by the first entity in the first federated learning task, and the at least one identity includes the first identity.

8. The method according to claim 6 or 7, characterized in that, The second response message includes the first identity.

9. The method according to any one of claims 6 to 8, characterized in that The second request message further includes information about a third network element, and the third network element supports coordinating the first entity to perform the first federated learning task.

10. The method according to any one of claims 1 to 9, characterized in that The method further includes: The first network element obtains information about a third network element and identity information of the third network element, where the identity information of the third network element is used to indicate that the third network element supports coordinating the first entity to perform the first federated learning task.

11. The method according to claim 10, characterized in that, The first network element obtains information of a third network element and identity information of the third network element, including: The first network element receives the information of the third network element and the identity information of the third network element from a second network element.

12. The method according to claim 10 or 11, characterized in that The method further includes: The first network element sends a fifth request message to the third network element, where the fifth request message is used to request the third network element to coordinate the first entity to execute the first federated learning task; The first network element receives a fifth response message from the third network element, where the fifth response message is used to indicate that the third network element agrees to coordinate the first entity to execute the first federated learning task.

13. The method according to any one of claims 10 to 12, characterized in that Before the first network element sends a first request message to the first entity, the method further includes: The first network element sends a sixth request message to the third network element, where the sixth request message is used to request to create or coordinate the first federated learning task, and the sixth request message includes the first entity and the first identity.

14. The method according to any one of claims 1 to 13, characterized in that Before the first network element determines the first entity and the first identity, the method further includes: The first network element obtains a first analysis identifier, where the first analysis identifier corresponds to the first federated learning task; The first network element triggers the first federated learning task according to the first analysis identifier.

15. A communication method, characterized in that, Including: The second network element receives a third request message, where the third request message is used to obtain an entity that supports executing the first federated learning task, and the second network element supports discovering an entity that executes the first federated learning task; The second network element determines at least one candidate entity and identity information of the at least one candidate entity, where the identity information of the at least one candidate entity is used to indicate the identity supported by the at least one candidate entity in the first federated learning task; The second network element sends a third response message, where the third response message includes the at least one candidate entity and the identity information of the at least one candidate entity.

16. The method according to claim 15, characterized in that, The third request message includes multiple types, and the multiple types correspond to the at least one candidate entity.

17. The method according to claim 15 or 16, characterized in that, The method further includes: The second network element sends information of the third network element and identity information of the third network element to the first network element, where the identity information of the third network element is used to indicate that the third network element supports coordinating the first entity to execute the first federated learning task.

18. The method according to any one of claims 15 to 17, characterized in that, The method further includes: The second network element receives a registration request message of at least one second entity, where the registration request message includes identity information of the at least one second entity, the at least one second entity supports executing the first federated learning task, and the at least one second entity includes the at least one candidate entity.

19. The method according to any one of claims 15 to 18, characterized in that, The second network element receives the third request message, including: The second network element receives the third request message from the first network element, where the first network element supports initiating the first federated learning task; or, The second network element receives the third request message from the third network element, where the third network element supports coordinating the first entity to execute the first federated learning task, and the at least one candidate entity includes the first entity.

20. A communication method, characterized in that, Including: A third network element obtains a first entity and a first identity, where the first identity is used to indicate the identity of the first entity in performing a first federated learning task, and the third network element supports coordinating the first entity to perform the first federated learning task; The third network element sends a seventh request message to the first entity, where the seventh request message is used to request the execution of the first federated learning task, and the seventh request message includes the first identity.

21. The method according to claim 20, wherein The third network element obtaining the first entity and the first identity includes: The third network element receives the first entity and the first identity from a first network element, and the first network element supports initiating the first federated learning task.

22. The method according to claim 21, wherein The third network element receiving the first entity and the first identity from the first network element includes: The third network element receives a sixth request message from the first network element, where the sixth request message is used to request the creation or coordination of the first federated learning task, and the sixth request message includes the first entity and the first identity.

23. The method according to claim 21 or 22, characterized in that Before the third network element receives the first entity and the first identity information from the first network element, the method further includes: The third network element sends at least one candidate entity and identity information of the at least one candidate entity to the first network element, where the identity information of the at least one candidate entity is used to indicate the identity supported by the at least one candidate entity in the first federated learning task, and the at least one candidate entity includes the first entity.

24. The method according to claim 23, wherein Before the third network element sends at least one candidate entity and identity information of the at least one candidate entity to the first network element, the method further includes: The third network element receives a fourth request message from the first network element, where the fourth request message is used to request the preparation for the execution of the first federated learning task; The third network element obtains the at least one candidate entity and the identity information of the at least one candidate entity; The third network element sends an eighth request message to the at least one candidate entity, where the eighth request message is used to request the at least one candidate entity to execute the first federated learning task, and the eighth request message includes the identity information of the at least one candidate entity; The third network element receives an eighth response message from the at least one candidate entity, where the eighth response message is used to indicate that the at least one candidate entity agrees to support executing the first federated learning task with the identity indicated by the identity information of the candidate entity.

25. The method according to claim 24, wherein The eighth request message further includes information of the third network element, and the eighth response message is further used to indicate the agreement for the third network element to negotiate the first federated learning task.

26. The method according to claim 24 or 25, characterized in that, The third network element obtaining the at least one candidate entity and the identity information of the at least one candidate entity includes: The third network element receives the at least one candidate entity and the identity information of the at least one candidate entity from a second network element, and the second network element supports discovering entities for performing the first federated learning task.

27. The method according to claim 26, wherein The third network element receiving the at least one candidate entity and the identity information of the at least one candidate entity from the second network element includes: The third network element sends a third request message to the second network element, and the third request message is used to obtain entities that support the execution of the first federated learning task; The third network element receives a third response message from the second network element, and the third response message includes the at least one candidate entity and the identity information of the at least one candidate entity.

28. The method according to any one of claims 21 to 27, characterized in that, Before the third network element receives the first entity and the first identity from the first network element, the method further includes: The third network element receives a fifth request message from the first network element, and the fifth request message is used to request the third network element to coordinate the execution of the first federated learning task by the first entity; The third network element sends a fifth response message to the first network element, and the fifth response message is used to indicate that the third network element agrees to coordinate the execution of the first federated learning task by the first entity.

29. A communication device, characterized in that, Comprising at least one module, the at least one module is used to execute the method according to any one of claims 1 to 28.

30. A communication device, characterized in that, Comprising: At least one processor, the at least one processor is used to execute a computer program or instruction so that the method according to any one of claims 1 to 28 is executed.

31. The communication device according to claim 30, wherein The communication device further includes a memory, and the memory is used to store the computer program or instruction; and / or, The communication device further includes a communication interface, the communication interface is coupled to the at least one processor, and the communication interface is used to input and / or output information.

32. The communication device according to claim 30 or 31, characterized in that, The communication device is a chip or a chip system.

33. A computer-readable storage medium, characterized in that, Computer program code or instructions are stored on the computer-readable storage medium, and when the computer program code or instructions run on a computer, the method according to any one of claims 1 to 28 is executed.

34. A computer program product, characterized in that, Containing instructions, when the instructions are run, the method according to any one of claims 1 to 28 is executed.

35. A communication system, characterized in that, Including at least one of a first network element, a second network element, or a third network element, wherein the first network element is used to execute the method according to any one of claims 1 to 14, the second network element is used to execute the method according to any one of claims 15 to 19, and the third network element is used to execute the method according to any one of claims 20 to 28.

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