Method, device and equipment for determining candidate members

Abstract

The present application discloses a method, apparatus and device for determining a candidate member, which belongs to the field of communications technology. A method for determining a candidate member in an embodiment of the present application includes: a first network element receiving a request message sent by a second network element, the request message including screening information; the first network element determining one or more first devices as candidate members capable of participating in federated learning based on the screening information; and the first network element sending a response message to the second network element, the response message including an identifier of the candidate member, where the screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a radio access method, a signal quality requirement, a traffic range, member type information, number information, area information and federated learning type information.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to a Chinese patent application filed on March 28, 2022, with application number 202210314821.7 and entitled "Method, Apparatus and Device for Determining Members to Participate in Federated Learning," and a Chinese patent application filed on April 29, 2022, with application number 202210476433.9 and entitled "Method, Apparatus and Device for Determining Candidate Members," the entire contents of which are incorporated herein by reference.

[0002] The present application relates to the field of communications technology, and more particularly to a method, apparatus and device for determining candidate members. [Background technology]

[0003] Federated learning refers to a method of performing machine learning modeling by coordinating different participants (also called participants, parties, data owners, or clients). In federated learning, participants do not need to disclose their own data to other participants or to a coordinator (also called a coordinator, server, parameter server, or aggregation server). Therefore, federated learning is excellent at protecting user privacy and ensuring data security, and can solve the data island problem.

[0004] After applying federated learning to the field of communications, how to select a number of appropriate members to perform federated learning and improve the training efficiency is currently a problem that needs to be solved urgently. Summary of the Invention [Problem to be solved by the invention]

[0005] The embodiments of the present application provide a method, device and apparatus for determining candidate members, which can solve the problem that participating members in federated learning do not meet the requirements, causing the training efficiency of federated learning to be relatively low. [Means for solving the problem]

[0006] A first aspect provides a method for determining candidate members, the method comprising: receiving, by a first network element, a request message sent by a second network element, the request message including screening information; The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information; sending a response message from the first network element to the second network element, the response message including an identifier of the candidate member; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0007] A second aspect provides a method for determining candidate members, the method comprising: A second network element sends a request message to a first network element, the request message including screening information, and the request message is used to instruct the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information; receiving, by the second network element, a response message sent by the first network element, the response message including an identifier of the candidate member; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0008] A third aspect provides a candidate member determination device, the device comprising: a receiving module for receiving a request message sent by a second network element, the request message including screening information; a processing module for determining one or more first devices as candidate members capable of participating in federated learning based on the screening information; a sending module for sending a response message to the second network element, the response message including an identifier of the candidate member; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0009] A fourth aspect provides a candidate member determination device, the device comprising: a sending module for sending a request message to a first network element, the request message including screening information, the request message being used to instruct the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information; a receiving module for receiving a response message sent by the first network element, the response message including an identifier of the candidate member; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0010] A fifth aspect provides a first network element, the first network element including a processor and a memory, the memory storing a program or instructions operable to run on the processor, the program or instructions, when executed by the processor, implementing the steps of the method of the first aspect.

[0011] A sixth aspect provides a first network element, the first network element including a processor and a communication interface, wherein the processor is adapted to determine one or more first devices as candidate members that can participate in federated learning based on screening information, and the communication interface is adapted to receive a request message sent by a second network element and to send a response message to the second network element, the response message including an identifier of the candidate member, wherein the request message includes screening information, and the screening information includes: a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0012] A seventh aspect provides a second network element, the network element including a processor and a memory, the memory storing a program or instructions operable to run on the processor, the program or instructions, when executed by the processor, implementing the steps of the method of the second aspect.

[0013] An eighth aspect provides a second network element, the second network element including a processor and a communication interface, wherein the communication interface is used to send a request message to a first network element, the request message including screening information, the request message instructing the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information, and is used to receive a response message sent by the first network element, the response message including identifiers of the candidate members; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0014] A ninth aspect provides a system for determining a candidate member, the system including a first network element and a second network element, the first network element being usable for performing steps of the method for determining a candidate member described in the first aspect, and the second network element being usable for performing steps of the method for determining a candidate member described in the second aspect.

[0015] A tenth aspect provides a readable storage medium having a program or instructions stored thereon, the program or instructions performing the steps of the method of the first aspect or the steps of the method of the second aspect when executed by a processor.

[0016] An eleventh aspect provides a chip, the chip including a processor and a communication interface, the communication interface coupled to the processor, the processor running a program or instruction to implement the method of the first aspect or to be used to implement the method of the second aspect.

[0017] A twelfth aspect provides a computer program / program product, the computer program / program product being stored in a storage medium, and the computer program / program product being executed by at least one processor to implement the steps of the candidate member determination method described in the first and second aspects. [Effects of the Invention]

[0018] In an embodiment of the present application, a first network element receives a request message sent by a second network element and determines one or more first devices as candidate members capable of participating in federated learning based on screening information included in the request message, and sends identifier information of the candidate members to the second network element. Here, the screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, an area of ​​interest (AOI), and a type of federated learning. The first network element can improve the training efficiency of federated learning by screening participating members suitable for federated learning based on the screening information. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a block diagram of a wireless communication system to which an embodiment of the present application can be applied. [Figure 2] 1 is a flowchart of a method for determining candidate members according to the present application. [Figure 3] 1 is a flowchart of a method for determining another candidate member according to the present application. [Figure 4] FIG. 1 is a signaling diagram of a method for determining candidate members according to the present application. [Figure 5] 1 is a structural schematic diagram of a candidate member determination device according to the present application; [Figure 6] 2 is a second structural schematic diagram of the candidate member determination device according to the present application. [Figure 7] 1 is a structural schematic diagram of a communication device according to an embodiment of the present application; [Figure 8] FIG. 2 is a hardware structural schematic diagram of a first network element according to an embodiment of the present application; [Figure 9] FIG. 2 is a hardware structural schematic diagram of a second network element according to an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0020] The following clearly describes the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application fall within the scope of protection of the present application.

[0021] The terms "first," "second," etc., used in the specification and claims of this application are intended to distinguish between similar objects and are not intended to describe a particular order or sequence. It should be understood that terms used in this manner are interchangeable where appropriate, so that embodiments of this application may be performed in orders other than those illustrated or described herein, and that objects distinguished by "first" and "second" generally are of the same type and do not limit the number of objects. For example, the first object may be one or more. Note that "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the related objects.

[0022] It should be noted that the techniques described in the embodiments of the present application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be applied to other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-Carrier Frequency Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in the embodiments of the present application are always used interchangeably, and the described techniques may be used in the above-mentioned systems and radio technologies as well as other systems and radio technologies. Although the following description describes a New Radio (NR) system for illustrative purposes and uses NR terminology in most of the following description, these techniques may be applied to applications other than NR system applications, such as 6th Generation (6G) communication systems.

[0023] 1 shows a block diagram of a wireless communication system to which the embodiments of the present application can be applied. The wireless communication system includes a terminal 11 and a network side device 12. In the present application, the terminal 11 may be referred to as a user equipment (UE). Here, the terminal 11 may be a terminal-side device such as a mobile phone, a tablet personal computer, a laptop computer (also called a notebook computer), a personal digital assistant (PDA), a palmtop computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, a vehicle-mounted equipment (VUE), a pedestrian-mounted equipment (PUE), a smart home (home equipment with wireless communication capabilities, such as a refrigerator, television, washing machine, or furniture), a game console, a personal computer (PC), a teller machine, or a self-service machine, and wearable devices include a smart watch, a smart wristband, a smart earphone, a smart glasses, a smart accessory (smart bracelet, smart hand chain, smart ring, smart necklace, smart ankle bracelet, smart anklet, etc.), a smart band, a smart garment, etc. It should be noted that the terminal 11 in the embodiment of the present application is not limited to a specific type. The network side device 12 may include an access network device or a core network device. Here, the access network device The vessel It may also be called a radio access network device, a radio access network (RAN), a radio access network function, or a radio access network unit. The vessel, a base station, a WLAN access point, or a WiFi node, and the base station may be called a Node B, an evolved Node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home B node, a home evolved B node, a transmitting receiving point (TRP), or any other appropriate term in the art. As long as the same technical effect is achieved, the base station is not limited to a specific technical term. It should be noted that the embodiments of this application only take base stations in an NR system as examples, and do not limit the specific type of base station.Core network devices include core network nodes, core network functions, mobility management entities (MMEs), access and mobility management functions (AMFs), session management functions (SMFs), user plane functions (UPFs), policy control functions (PCFs), policy and charging rules functions (PCRFs), edge application server discovery functions (EASDFs), unified data management (UDMs), unified data repository (UDRs), home subscriber servers (HSSs), centralized network configuration (CNCs), network repository functions (NRFs), network exposure functions (NEFs), local NEFs (or L-NEFs), binding support functions (BSFs), and application functions (Application Node Functions). It should be noted that the embodiments of the present application only take core network equipment in an NR system as an example, and do not limit the specific type of core network equipment.

[0024] The following describes in detail the method for determining candidate members according to the embodiments of the present application through several examples and application scenarios thereof in conjunction with the drawings.

[0025] Federated learning includes horizontal federated learning and vertical federated learning. Here, the essence of horizontal federated learning is the federation of samples, which is applied to scenarios where participants are in the same business but reach different clients. That is, when there is a lot of overlap in features but little overlap in samples, for example, the same service (e.g., mobility management (MM), session management (SM) service, or some other service) serving different users (e.g., each terminal, i.e., different samples) in the core network (CN) domain and the radio access network (RAN) domain within a communication network. Due to the same data features of different samples from federated participants, horizontal federation can obtain a better model by increasing the number of training samples.

[0026] The essence of vertical federation learning is feature federation, which can be applied to scenarios where there is a lot of sample overlap but little feature overlap, such as different services (e.g., MM and SM services, i.e., different features) serving the same user (e.g., terminal, i.e., the same sample) in the CN domain and the RAN domain in a communication network. Due to the different data features of the joint samples of the federated participants, vertical federation increases the feature dimension of the training sample and obtains a better model.

[0027] A federated learning system includes an administrator (server) and multiple participants. The administrator sends a model to each participant, updates the model based on the feedback from each participant, and then sends the updated model back to each participant for use in the next model training. Each participant has its own data, and to avoid sending local data to others, each participant uses the model sent by the administrator to train locally and then returns it to the administrator for model updating.

[0028] Illustratively, the model training process is as follows: Step 1: Each participant downloads the latest model from the administrator (server); Step 2: Each participant uses local data to train the model and uploads the encrypted gradients to the administrator (server), which then aggregates the gradients of each participant and updates the model parameters; Step 3: The administrator (server) returns the updated model to each participant; and step 4, in which each participant updates their respective model.

[0029] Steps 2 to 4 above involve multiple iterations / model updates, and model training is terminated under certain conditions, such as when a certain number of iterations have been completed or the calculated value of the model's loss function falls below a preset value.

[0030] 5GS has network data analysis function network element (network data analytics The NWDAF will collect data from each network element of the core network, network management systems, etc., and perform big data statistics, analysis, or smart data analysis to obtain network-side analysis or prediction data, thereby assisting each network element in more effective control of terminal access based on the data analysis results.

[0031] In the method for determining candidate members according to the present application, the NWDAF can collect data from other network elements and analyze this data, thereby improving the efficiency of federated learning by screening suitable participating members for federated learning based on the screening information and data analysis results.

[0032] It should be noted that "determining candidate members" in this application may be understood as "determining candidate members who can participate in federated learning."

[0033] 2 is a flowchart of a method for determining candidate members according to the present application, and the method for determining candidate members according to an embodiment of the present application will be described below in conjunction with FIG. 2. As shown in FIG. 2, the method includes:

[0034] Step 201: A first network element receives a request message sent by a second network element.

[0035] Here, the execution body of the candidate member determination method according to the embodiment of the present application is a first network element, and this first network element may be implemented in various forms. For example, the first network element described in the embodiment of the present application may include an NWDAF, and may of course be a network element that can collect data from each network element of a core network, a network management system, etc., and perform big data statistics, analysis, or smart data analysis. The second network element may include a task consumer network element, and may be a network element or device such as an application function network element (AF), a base station, a terminal, etc., or it may even be a third-party server.

[0036] Here, the request message includes screening information, which includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, area information, and federated learning type information. By screening more suitable participating members based on different dimensions of screening information, the efficiency of federated learning can be improved.

[0037] Here, the time period is used to indicate a selective time period for performing the federated learning, and the time period may be a time period in the past or a time period in the future. As can be understood, when the time period is a future time period, the first network element predicts the network connection state of each member in the future time period based on the acquired historical data. The network connection state includes a state where the member is already connected to the network and a state where the member is not connected to the network. It should be understood that the time period is generally flexibly set according to the time period for actually performing the federated learning.

[0038] The algorithm type is used to indicate the model training algorithm type that needs to be supported to perform the federated learning, and includes algorithm types related to artificial intelligence (AI) data analysis tasks related to machine learning and the like supported by each member, such as "deep learning algorithm," "linear regression algorithm," etc. It should be understood that, generally, based on the model's functions, members with algorithm types that match these functions are selected and supported as participating members in the federated learning.

[0039] The accuracy threshold is used to indicate the accuracy requirements of model training that must be met to perform the federated learning, and may be understood as the accuracy value that the model generated after training each member can achieve, such as the accuracy rate of the model, which may be understood as the percentage of correct predictions or judgments made by the model. It should be understood that, to ensure the accuracy of federated learning, several members with relatively high accuracy are generally selected as participating members in federated learning.

[0040] The wireless access method is used to indicate the wireless access method that needs to be selected for performing federated learning, and includes a method for connecting to or accessing the communication network of each member, such as a WLAN connected to a non-3GPP network (similar to connecting to a WiFi network), a 5G or 4G network connected to a 3GPP network, etc. It should be understood that, in order to ensure the stability of federated learning and reduce traffic consumption, members connected to some WLANs or WiFis are generally selected as participating members for federated learning.

[0041] The signal quality requirement is used to indicate a wireless signal quality requirement when performing the federated learning. The signal quality requirement may include a network signal strength threshold and / or a stability threshold when connected to each member's communication network. The signal quality requirement may include a WLAN network signal quality requirement, a 5G NR network signal quality requirement, and a 4G Long Term Evolution (LTE) network signal quality requirement. The signal quality requirement may be understood as a minimum requirement for the proportion of time that the signal strength must be maintained above a certain value, for example, the signal strength must be able to achieve the threshold requirement at least 90% of the time. It should be understood that to ensure the stability of federated learning, several members with relatively strong signal quality (e.g., relatively strong network signal strength and relatively good stability) are generally selected as members to participate in federated learning.

[0042] Here, when connected to a WLAN, the received signal strength indication Indicator The signal quality can be expressed using the mean value, variance, etc. of the RSSI (RSS Indicator Index), or the round-trip time (RTT) of the signal.

[0043] The traffic range is used to indicate the traffic usage range requirements of the candidate members and is the traffic value requirement consumed by each member during a predetermined time period. For example, it may be information such as the number of upload and download traffic, or may be the accumulated usage of the UE. It should be understood that, in order to reduce the pressure on each participating member when performing federated learning, some members with stable network conditions and relatively low traffic consumption are generally selected as participants in federated learning.

[0044] The member type information is used to indicate the type request of the candidate member to participate in the federated learning, and the member type information may be understood as the type of the candidate member to participate in the current federated learning, such as a terminal, a core network element (e.g., NWDAF), or a base station.

[0045] The area of ​​interest (AOI) is used to indicate the area where the candidate member is located, and the AOI may be the area where the candidate member participating in federated learning is located, or an area range of attention or interest, which may be longitude and latitude, or one or more cells / tracking areas (TA), etc.

[0046] The number information is used to indicate the number of candidate members required, and this number information may be understood as the number of members participating in federated learning, i.e., the number of candidate members that the first network element needs to determine. The first network element can determine this number of candidate network elements. This number information may be the minimum number of candidate members required, i.e., the number of candidate members to be determined must not be smaller than the minimum number of candidate members required. This number information may also be the maximum number of candidate members required, i.e., the number of candidate members to be determined must not be larger than the maximum number of candidate members required.

[0047] The federated learning type information is used to indicate whether the federated learning belongs to a vertical federation or a horizontal federation. Optionally, the request message may further include instruction information. This instruction information is used to instruct the execution of a task to determine candidate members to participate in the federated learning, i.e., indicates that the current task is used for federated learning or member selection for federated learning. After receiving the request message, the first network element can know, based on the request message or the instruction information in the request message, that it needs to execute the task to determine candidate members to participate in the federated learning.

[0048] Step 202: The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information.

[0049] In this step, after receiving the request message, the first network element determines one or more first devices that meet the screening information by screening from multiple second devices based on the screening information in the request message, and determines these first devices as candidate members that can participate in federated learning.

[0050] Optionally, the first device includes at least one of a first device in a network connection state during the time period, a first device that supports federated learning during the time period, a first device that supports an algorithm type, a first device that supports federated learning using the algorithm type, a first device whose model training accuracy information is greater than an accuracy threshold, a first device that supports a wireless access method, a first device in the wireless access method, a first device that supports federated learning in the wireless access method, a first device whose signal quality is greater than a signal quality threshold, a first device whose signal quality is greater than a signal quality threshold requirement, a first device whose consumed traffic is in a traffic range, a first device that meets the type requirement of the candidate member, a first device located within the AOI, and a first device whose type of federated learning is the same as the type of federated learning included in the screening information.

[0051] Here, when the screening information includes a time period, the first device is a first device that is in a network connection state during the time period, or the first device is a first device that supports federated learning during the time period; when the screening information includes an algorithm type, the first device is a first device that supports the algorithm type, or the first device is a first device that supports federated learning using the algorithm type; when the screening information includes an accuracy threshold, the first device is a first device whose accuracy information of model training is greater than the accuracy threshold; and when the screening information includes a wireless access method, the first device is a first device that supports the wireless access method. The first device may be a first device in the wireless access method, or the first device may be a first device supporting federated learning in the wireless access method. If the screening information includes a signal quality threshold, the first device is a first device whose signal quality is greater than the signal quality threshold. If the screening information includes a signal quality requirement, the first device is a first device whose signal quality is greater than the signal quality requirement. If the screening information includes a traffic range, the first device is a first device whose consumed traffic is in the traffic range. If the screening information includes member type information, the first device is a first device that satisfies the type requirement of the candidate member. As can be understood, the type of the first device is the same as the member type information indicated in the screening information. If the screening information includes an AOI, the first device is a first device located within the AOI. If the screening information includes federated learning type information, the federated learning type of the first device is the same as the federated learning type information included in the screening information.

[0052] Also, if the screening information includes a device type, the first device is the first device of the device type, and if the screening information includes area information, the first device is the first device in this area information.

[0053] It should be understood that when the screening information includes at least two pieces of information, the first device is a device that satisfies at least two pieces of corresponding screening information. For example, when the screening information includes a time period and a supported algorithm type, the first device is the first device that is in a network-connected state during the time period and supports the algorithm type. When the screening information includes at least two other pieces of information, the same is true for the case where the screening information includes a time period and a supported algorithm type, and no further description is given here.

[0054] In the above embodiment, the screening information can be used to screen first devices that meet the screening information from at least one second device as candidate network elements, thereby selecting appropriate members that can participate in federated learning, and contributing to improving the efficiency of federated learning training.

[0055] Optionally, when determining candidate members, the first network element can determine a data type corresponding to the screening information, obtain attribute information corresponding to each of at least one device from at least one third network element based on the data type, and determine candidate members based on the attribute information and the screening information.

[0056] Optionally, the data type includes at least one of network connection information and corresponding time period, algorithm type, wireless access method, signal quality, and traffic.

[0057] Specifically, if the screening information includes a location time zone, the data type corresponding to the screening information is network connection information and a time zone corresponding to the network connection state; if the screening information includes a supported algorithm type, the data type corresponding to the screening information is an algorithm type; if the screening information includes an accuracy threshold, the data type corresponding to the screening information is accuracy information; if the screening information includes a wireless access method, the data type corresponding to the screening information is a wireless access method; if the screening information includes a signal quality threshold, the data type corresponding to the screening information is a signal quality; if the screening information includes a traffic range, the data type corresponding to the screening information is a traffic; if the screening information includes a member type, the data type corresponding to the screening information is a device type; if the screening information includes area information, the data type corresponding to the screening information is location information.

[0058] As can be understood, when the screening information includes a member type, the screening information included in the request message varies depending on the member type. That is, the screening information is related to the member type. For example, when the member type includes a terminal, the screening information may include at least one of a time period, a supported algorithm type, an accuracy threshold, a radio access scheme, a signal quality threshold, a traffic range, and area information. When the member type includes a network side device, such as a base station or a core network device, the screening information may include at least one of a supported algorithm type and an accuracy threshold.

[0059] After determining the data type, the first network element acquires attribute information corresponding to each of the at least one second device from at least one third network element based on the data type. Here, the third network element includes a plurality of different network elements, and it should be understood that the acquired attribute information differs. The first network element acquires the attribute information from the different third network elements. For example, the first network element acquires information such as the time the terminal is connected to the WLAN and whether it is connected to the WLAN from a session management function (SMF) of the third network element. The first network element acquires information such as the signal quality of the WLAN connection from the third network element. Here, the third network element may be a network management device, such as an operation administration and maintenance (OAM), and obtains the algorithms and accuracy supported by each candidate network element from the unified data management (UDM) or data collection - application function (DC-AF) of the third network element, and obtains terminal traffic information, etc. from the user plane function (UPF) of the third network element, or the first network element obtains UE accumulated usage information from the SMF or Charging Function (CHF).

[0060] As can be understood, when the data type includes network connection information and a corresponding time period, the attribute information is the current network connection status and the time during which the at least one device is in the network connection state, for example, whether it is connected to the network and during which time period it is in the network connection state. When the data type includes algorithm type, the attribute information is the currently supportable algorithm corresponding to each of the at least one device, for example, whether it supports a deep learning algorithm or a linear regression algorithm. When the data type includes accuracy information, the attribute information is the currently supportable accuracy corresponding to each of the at least one device, for example, the achievable accuracy of a model after each device has trained the model. When the data type includes wireless access method, the attribute information is the type of network method currently accessed by the at least one device, for example, whether a WLAN or a 5G network is currently accessed. When the data type includes signal quality, the attribute information is the signal quality of the currently accessed network of the at least one second device or the signal quality of the network accessed during a predetermined time period. When the data type includes traffic information, the attribute information is the traffic consumed during a predetermined time period corresponding to each of the at least one second device. When the data type includes a device type, the attribute information is a type corresponding to each of the at least one second device, such as a terminal, a base station, or a core network device. When the data type includes location information, the attribute information is a location where each of the at least one second device is located, such as a current geographic location, or a cell or TA where it is located.

[0061] After obtaining this attribute information, the attribute information corresponding to each of at least one second device is matched with the screening information, and candidate members are determined based on this screening information, for example, a first device that satisfies the screening information can be determined as a candidate member.

[0062] For example, assume that the device type included in the screening information is a terminal, the area is cell A, time period A, and the radio access method is WiFi connected to non-3GPP, the first network element obtains corresponding attribute information based on the screening information, and selects a WiFi terminal that is in cell A, has a network connection state during time period A, and is connected to non-3GPP as a candidate member based on the obtained attribute information.

[0063] In this embodiment, the first network element obtains a data type corresponding to the screening information and obtains attribute information of each second device from at least one third network element, and determines candidate members based on the attribute information and screening information. The first network element can determine suitable candidate members to participate in federated learning from at least one second device based on the preset screening information, thereby improving the efficiency of federated learning.

[0064] In one possible implementation manner, the first network element can directly determine devices that meet the screening information as candidate members, and by default, all candidate network elements that meet this screening information can participate in federated learning, and the first network element can send identifiers of all candidate members that meet this screening information to the second network element.

[0065] In another possible implementation manner, in order to screen more suitable participants, when determining candidate members based on the attribute information and the screening information, the first network element can further screen based on the willingness information of each second device. For example, the first network element can obtain first instruction information from the third network element and determine one or more first devices as candidate members from the second devices based on the screening information and the first instruction information. Here, the first instruction information is used to represent the willingness information of each device to participate in federated learning, and the willingness information represents whether each device wants to participate in federated learning.

[0066] Here, the first device is a device that satisfies the screening information and wants to participate in federated learning.

[0067] For example, each device corresponds to its own first instruction information, and for a certain device, if the corresponding first instruction information is 0, it indicates that this device wants to participate in federated learning, and if the first instruction information is 1, it indicates that this device does not want to participate in federated learning; alternatively, if the first instruction information is 1, it indicates that this device wants to participate in federated learning, and if the first instruction information is 0, it indicates that this device does not want to participate in federated learning. Of course, the first instruction information may adopt other numerical values ​​to indicate the willingness of each device to participate in federated learning.

[0068] After obtaining the first instruction information, the first network element matches the attribute information of each device with the screening information, and determines the devices that meet the screening information and wish to participate in the federated learning instructed by the first instruction information as candidate members.

[0069] In this embodiment, the first network element meets the screening information based on the screening information and the first instruction information, and screens devices that wish to participate in federated learning as candidate members, thereby contributing to improving the efficiency of subsequent federated learning.

[0070] Optionally, when the first network element determines one or more first devices as candidate members from the second devices based on the screening information and the first instruction information, it can further obtain capability information of each second device to participate in federated learning from the third network element, thereby determining candidate members based on the screening information, the first instruction information and the capability information.

[0071] Here, the capability information includes at least one of the wireless access method participating in federated learning, the area participating in federated learning, the time participating in federated learning, algorithm information that can support participation in federated learning, accuracy information that can be achieved by participation in federated learning, and the type of participation in federated learning.

[0072] Here, the type of participation in federated learning may include a desire to participate in horizontal federated learning or a desire to participate in vertical federated learning.

[0073] In one embodiment, the first network element determining one or more first devices as candidate members capable of participating in federated learning based on the screening information includes: The first network element acquires, from a third network element, information indicating that the first device intends to participate in the federated learning; The first network element determines, based on the intention information and the screening information, a first device that wishes to participate in the federated learning and matches the screening information as a candidate member that can participate in the federated learning.

[0074] In one embodiment, the first network element determining one or more first devices as candidate members capable of participating in federated learning based on the screening information includes: The first network element acquires federated learning capability information of the first device, the capability information including at least one of a radio access method participating in federated learning, an area participating in federated learning, a time period participating in federated learning, algorithm information that can be supported by participating in federated learning, accuracy information that can be achieved by participating in federated learning, and a type participating in federated learning; The first network element determines candidate members that can participate in federated learning from the first device based on the capability information and the screening information, and the capability information of the candidate members matches the screening information.

[0075] Illustratively, a first network element may obtain federated learning capability information of the first device from a fourth network element, where the fourth network element may be at least one of an NRF, a UDM, a Data Collection Coordination Function (DCCF), and an AMF.

[0076] The ability information of the candidate member matches the screening information, The wireless access method in which the candidate member participates in federated learning is the same as the wireless access method included in the screening information; and The area in which the candidate member will participate in the federated learning is located within the AOI included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and The algorithm types that can support the candidate member's participation in federated learning are included in the algorithm types included in the screening information; The accuracy information that can be achieved by the candidate member participating in the federated learning is higher than the accuracy threshold included in the screening information; and the type of federated learning in which the candidate member participates is the same as the type of federated learning included in the screening information.

[0077] Optionally, the first network element can further obtain the capability information of each device to participate in federated learning from the third network element, thereby matching the attribute information of each device with the screening information, and determining as candidate members the devices that wish to participate in federated learning as instructed by the first instruction information and whose capability information to participate in federated learning satisfies the screening information, thereby improving the efficiency of subsequent federated learning.

[0078] Here, the ability information to participate in the federated learning satisfies the screening information. The wireless access method of the candidate member participating in the federated learning is the same as the wireless access method included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and The algorithm types that can support the candidate member's participation in federated learning are included in the algorithm types included in the screening information; The accuracy information that can be achieved by the candidate member participating in the federated learning is higher than the accuracy threshold included in the screening information; The type of federated learning in which the candidate member participates is the same as the type of federated learning included in the screening information.

[0079] For example, the screening information may include supported algorithm types, where the supported algorithm types are deep learning algorithms. The first network element may screen 80 devices that wish to participate in federated learning based on the first instruction information, and may further screen the 80 devices based on the algorithm information that can be supported for participation in federated learning obtained from the third network element. For example, if it is obtained that 50 of the 80 devices can support not only deep learning algorithms but also linear regression algorithms when participating in federated learning, and 30 devices only support deep learning algorithms when participating in federated learning, the first network element may further screen the first device that can support not only deep learning algorithms but also linear regression algorithms as a candidate network element based on the algorithm types that can be supported when participating in federated learning.

[0080] It should be understood that in cases where the capability information includes other information, the implementation method is similar to the implementation method of the algorithm type that can support participation in federated learning included in the capability information, and will not be described further here.

[0081] Here, the first network element can obtain information on the willingness and ability of each device to participate in federated learning from the third network elements UDM / DCAF / NRF, where UDM / DCAF / NRF are network elements with memory capabilities.

[0082] Optionally, the request message sent by the second network element further includes second indication information, which is used to indicate a service type corresponding to the federated learning. When determining candidate members, the first network element may determine one or more first devices as candidate members from the plurality of second devices based on the screening information and the second indication information, where the first devices are devices that can support a service corresponding to the service type.

[0083] Specifically, the second instruction information is analytics It may be an ID, which is used to indicate the service type corresponding to federated learning. In data analysis tasks (i.e., other tasks handled by NWDAF), this analytics The ID may be as follows: "UE mobility" is used to indicate that the current task is an analysis task related to UE mobility, and "NF load" is used to indicate that the current task is an analysis task related to network element load.

[0084] Generally, when the service types corresponding to federated learning are different, the selected candidate members may be different. Therefore, when selecting candidate members, the first network element may further consider screening information and analytics Based on the service type indicated by the ID, a first device capable of supporting a service corresponding to the service type can be screened as a candidate member from among a plurality of second devices. For example, the second instruction information is used to indicate that the service type corresponding to federated learning is an analysis task related to terminal mobility, and the selected first device is a device capable of supporting the analysis task related to terminal mobility.

[0085] In this embodiment, the accuracy of the selected candidate members can be improved by further screening candidate members based on the service type corresponding to federated learning, thereby improving the training efficiency of federated learning and the accuracy of the model after training.

[0086] In one embodiment, the first network element determining one or more first devices as candidate members capable of participating in federated learning based on the screening information includes: The first network element obtains network status information corresponding to the first device, wherein the network status information comprises: type information of the first device; location information of the first device; wireless access method information in which the first device is located; and wireless signal quality information of the first device; The first network element determines candidate members that can participate in federated learning from the first device based on network status information corresponding to the first device and the screening information, and the network status information corresponding to the candidate members matches the screening information.

[0087] It should be mentioned that the first network element may obtain network status information corresponding to the first device from a third device, and the third device may be an AMF, a UDM, an NRF, a PCF, a RAN, or an operation, administration, and maintenance (OAM) device. For example, the first network element may obtain type information of the first device from a UDM or an NRF, or the first network element may obtain radio access method information in which the first device is located from an AMF or a PCF, or the first network element may obtain radio signal quality information of the first device from a RAN or an OAM, or ... type information of the first device from a PCF or an AMF, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an AMF, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an AMF, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an AMF, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an OAM, or the first network element may obtain type information of the first device from a PCF or an AMF, or the first network element may obtain type information of the first device element may obtain UE accumulated usage information from the SMF or CHF.

[0088] Here, the network status information corresponding to the candidate member matches the screening information, The wireless access system in which the candidate member is located is the same as the wireless access system included in the screening information; the candidate member's wireless signal quality information is greater than the signal quality requirement included in the screening information; and The position of the candidate member is located within an AOI included in the screening information; and the type of the candidate member is included in member type information included in the screening information.

[0089] Optionally, the request message further includes sorting instruction information, which is used to instruct sorting the candidate members according to second information, where the second information includes at least one of the signal quality of the candidate members, the accuracy information of the candidate members, and the traffic information of the candidate members, and when the request message includes the sorting instruction information, the identifier order of the candidate members in the response message is the order obtained after sorting according to the second information.

[0090] Specifically, when the request message includes sorting instruction information, after determining the candidate members, the first network element sorts the determined candidate members in ascending or descending order according to the second information in the sorting instruction information, for example, sorting in ascending or descending order according to the signal quality of the candidate members, sorting in ascending or descending order according to the accuracy information of the candidate members, or sorting in ascending or descending order according to the traffic information of the candidate members.

[0091] When the first network element sends a response message to the second network element, the identifier order of the candidate members in the response message is the order obtained after sorting according to the second information.

[0092] In this embodiment, the first network element sorts the determined candidate members according to the second information; thus, after receiving the candidate member identifier information sent by the first network element, the second network element can screen candidate members that more closely match its requirements based on this sorting information, and participate in subsequent federated learning, thereby improving the efficiency of federated learning.

[0093] Optionally, the request message further includes packet indication information, which is used to instruct the candidate member to packetize according to third information, where the third information includes at least one of the area where the candidate member is located, the time period when the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access scheme of the candidate member, and the signal quality of the candidate member, and when the request message includes the packet indication information, the identifier of the candidate member in the response message is set according to the third information. Packetization The identifier obtained after

[0094] Specifically, if the request message includes packet indication information, the first network element, after determining the candidate members, packetizes the determined candidate members according to the third information in the packet indication information. For example, the packetization may be performed according to the region in which the candidate members are located, such as grouping candidate members in the same region into the same group. Alternatively, the packetization may be performed according to the time period in which the candidate members are in a network connection state, such as grouping candidate members in a network connection state in the same time period, or according to the algorithm type supported by the candidate members, such as grouping network elements supporting the same algorithm type into the same group. Alternatively, the packetization may be performed according to the accuracy information of the candidate members, such as grouping candidate members with the same accuracy information into the same group, or according to the radio access scheme of the candidate members, such as grouping candidate members with the same radio access scheme into the same group, or according to the signal quality of the candidate members, or according to the traffic information of the candidate members, or according to the time period in which the candidate members are available for federated learning, such as grouping candidate members in the same time period in which federated learning is available, such as grouping candidate members in the same time period, such as 10:00 p.m. to 12:00 noon.

[0095] When the first network element sends a response message to the second network element, the identifier of the candidate member in the response message is the identifier obtained after packetizing according to the third information.

[0096] In this embodiment, the first network element packages the determined candidate members according to the third information. In this way, after receiving the candidate member identifier information sent by the first network element, the second network element can screen candidate members that better meet its requirements based on the package information and participate in subsequent federated learning, thereby improving the efficiency of federated learning.

[0097] Step 203: The first network element sends a response message to the second network element, where the response message includes the identifier of the candidate member.

[0098] In this step, after determining the candidate members who can participate in federated learning, the first network element can send the identifiers of these candidate members in a response message to the second network element, where the identifiers of the candidate members can be Subscriber Permanent Identifiers (SUPIs) or network protocol (Internet Protocol (IP)) identifiers of the candidate members. address and the identifier of the potential member may further be a Generic Public Subscription Identifier (GPSI), an International Mobile Subscriber Identity (IMSI), an AF specific UE ID, or a UE IP address.

[0099] Optionally, the response message further includes at least one of the area where the candidate member is located, the time period during which the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access method of the candidate member, the signal quality of the candidate member, the traffic information of the candidate member, and the time period during which the candidate member is available for federated learning.

[0100] Here, the region where the candidate member is located may include the location of the candidate member, such as the longitude and latitude or the cell.

[0101] The time period during which the candidate members can perform federated learning is a predicted result analyzed by the first network element based on the intention information and capability information of each device.

[0102] The signal quality of a candidate member may be expressed using the mean, variance or RTT of RSSI when connected to the WLAN.

[0103] The traffic information of the potential member may include information such as the traffic value consumed in a predetermined time period, for example, the number of uploads and downloads.

[0104] Optionally, the response message may further include second indication information, and the second indication information may include: analytics It may be an ID, which is used to indicate the service type that corresponds to federated learning.

[0105] Optionally, the response message may further include the coverage time ratio of the network connection, for example, how much wifi connection there is throughout the day.

[0106] Optionally, the response message may further include address information of the candidate member, so that the second network element can find the candidate member based on the address information, thereby performing connection and federated learning.

[0107] Optionally, the response message may further include number information, which may be understood as the number of members participating in federated learning, i.e., the number of candidate members that the first network element needs to determine.

[0108] Furthermore, after receiving the response message sent by the first network element, the second network element selects target members to participate in the real federated learning according to the situation of performing the actual federated learning training.

[0109] For example, the second network element can determine the members to perform federated learning based on the signal quality of the candidate members in the response message, and can select the top 100 members with the best signal qualities to perform federated learning. Of course, the method of determining the target member based on other information in the response message is similar to the method of determining the target member based on signal quality, and will not be further described here.

[0110] Alternatively, if the request message sent by the second network element to the first network element includes number information, and the response message returned by the first network element includes a target number of candidate members, the second network element may directly select the target number of candidate members as target members. For example, if the request message includes a request for 50 federated learning members and the response message returned by the first network element carries 50 candidate members, the second network element may directly select the 50 members as target members.

[0111] Furthermore, after determining the target members, the second network element connects with these target members based on the identifier information of the target members to perform federated learning.

[0112] In this embodiment, the second network element can further screen target members to participate in true federated learning based on the candidate members returned by the first network element, thereby making the screened target members more suitable to participate in federated learning and improving the efficiency of federated learning.

[0113] A method for determining candidate members according to an embodiment of the present application includes receiving a request message sent by a second network element, determining one or more first devices as candidate members capable of participating in federated learning based on screening information included in the request message, and sending identifier information of the candidate members to the second network element. Here, the screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, area information, and federated learning type information. The first network element can improve the efficiency of federated learning by screening participating members suitable for federated learning based on the screening information.

[0114] 3 is a flowchart of another method for determining candidate members according to the present application. In this embodiment, the execution body is the second network element. As shown in FIG. 3, the method includes:

[0115] Step 301: A second network element sends a request message to a first network element.

[0116] Here, the request message includes screening information, and the request message is used to instruct the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information.

[0117] Here, the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0118] Step 302: The second network element receives the response message sent by the first network element, where the response message includes an identifier of the candidate member.

[0119] The specific implementation process and beneficial effects in the embodiments of the present application may refer to the content of the embodiment shown in FIG. 2, and will not be further described here.

[0120] Optionally, the first device comprises: a first device that is in a network-connected state during the time period; a first device that supports performing federated learning during the time period; a first device that supports the algorithm type; a first device that supports federated learning using the algorithm type; A first device whose accuracy information of model training is greater than the accuracy threshold; a first device in the wireless access system; a first device that supports federated learning in the wireless access scheme; a first device whose signal quality is greater than a signal quality requirement; a first device whose consumed traffic is in the traffic range; a first device that meets the type requirements of the candidate member; a first device located within the AOI; and a first device whose type of associative learning is the same as the type information of associative learning included in the screening information.

[0121] Optionally, the request message further includes instruction information, which is used to instruct the execution of a task to determine candidate members to participate in federated learning, or to indicate that the current task is for federated learning or member selection for federated learning.

[0122] Optionally, the request message further includes sorting instruction information, the sorting instruction information is used to instruct sorting the candidate members according to second information, the second information including at least one of signal quality of the candidate members, accuracy information of the candidate members, and traffic information of the candidate members; The identifiers of the candidate members in the response message are the identifiers obtained after sorting according to the second information.

[0123] Optionally, the request message further includes packet indication information, which is used to instruct the candidate member to packetize according to third information, and the third information includes at least one of the following: an area where the candidate member is located; a time period when the candidate member is in a network connection state; an algorithm type supported by the candidate member; accuracy information of the candidate member; a radio access scheme of the candidate member; and a signal quality of the candidate member; The identifier of the candidate member in the response message is the identifier obtained after packetizing according to the third information.

[0124] Optionally, the response message further includes the area where the candidate member is located, the time period during which the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access method of the candidate member, the signal quality of the candidate member, the traffic information of the candidate member, and the time period during which the candidate member is available for federated learning.

[0125] Optionally, the method further comprises: The method further includes the second network element determining, based on the identifiers of the candidate members, target members to participate in the federated learning.

[0126] A method for determining candidate members according to an embodiment of the present application includes receiving a request message sent by a second network element, determining one or more first devices as candidate members capable of participating in federated learning based on screening information included in the request message, and transmitting identifier information of the candidate members to the second network element. Here, the screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, area information, and federated learning type information. The first network element can improve the efficiency of federated learning by screening suitable participating members for federated learning based on the screening information. Furthermore, the second network element can further screen based on the candidate members sent by the first network element to obtain members suitable for truly participating in federated learning, thereby improving the efficiency of federated learning.

[0127] The specific implementation process and technical effect of the method of this embodiment are similar to those of the method embodiment on the first network element side, and you may refer to the detailed introduction in the method embodiment on the first network element side, and no further description will be given here.

[0128] 4 is a signaling diagram of a method for determining candidate members according to the present application. As shown in FIG. 4, the method includes:

[0129] Step 401: A second network element sends a request message to a first network element.

[0130] Here, the request message includes screening information, which includes at least one of a time period, an algorithm type, an accuracy threshold, a radio access method, a signal quality requirement, a traffic range, member type information, number information, area information, and federated learning type information.

[0131] Step 402: The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information.

[0132] Step 403: The first network element sends a response message to the second network element, where the response message includes an identifier of the candidate member.

[0133] A method for determining candidate members according to an embodiment of the present application includes receiving a request message sent by a second network element, determining one or more first devices as candidate members capable of participating in federated learning based on screening information included in the request message, and sending identifier information of the candidate members to the second network element. Here, the screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, an area of ​​interest (AOI), and a type of federated learning. The first network element can improve the training efficiency of federated learning by screening participating members suitable for federated learning based on the screening information.

[0134] The specific implementation process and technical effect of the method of this embodiment are similar to those of the method embodiment on the first network element side, and you may refer to the detailed introduction in the method embodiment on the first network element side, and no further description will be given here.

[0135] In the method for determining candidate members according to the embodiment of the present application, the execution body may be a device for determining candidate members. In the embodiment of the present application, the device for determining candidate members according to the embodiment of the present application will be described by taking the device for determining candidate members as an example of executing the method for determining candidate members.

[0136] 5 is a structural diagram of a candidate member determination device according to the present application. As shown in FIG. 5, the candidate member determination device according to this embodiment includes: a receiving module 11 for receiving a request message sent by a second network element, the request message including screening information; a processing module 12 for determining one or more first devices as candidate members capable of participating in federated learning based on the screening information; a sending module 13 for sending a response message to the second network element, the response message including an identifier of the candidate member; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0137] In the apparatus of this embodiment, the receiving module receives a request message sent by a second network element, the request message including screening information, the processing module determines one or more first devices as candidate members capable of participating in federated learning based on the screening information, and the sending module transmits a response message to the second network element, the response message including identifiers of the candidate members. Here, the screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, an area of ​​interest (AOI), and a type of federated learning. The candidate member determination device can improve the training efficiency of federated learning by screening participating members suitable for federated learning based on the screening information.

[0138] Optionally, the first device comprises: a first device that is in a network-connected state during the time period; a first device that supports performing federated learning during the time period; a first device that supports the algorithm type; a first device that supports federated learning using the algorithm type; A first device whose accuracy information of model training is greater than the accuracy threshold; a first device in the wireless access system; a first device that supports federated learning in the wireless access scheme; a first device whose signal quality is greater than a signal quality requirement; a first device whose consumed traffic is in the traffic range; a first device that meets the type requirements of the candidate member; a first device located within the AOI; and a first device whose type of associative learning is the same as the type information of associative learning included in the screening information. determining a data type corresponding to the screening information; acquiring attribute information corresponding to each of the at least one device from the at least one third network element based on the data type; The candidate members are determined based on the attribute information and the screening information.

[0139] Optionally, the data type is: The information includes at least one of network connection information and corresponding time period, algorithm type, accuracy information, radio access method, signal quality, and traffic.

[0140] Optionally, the processing module 12 Acquiring first indication information from a third network element for each second device to indicate its willingness to participate in federated learning, the willingness information indicating whether each second device wishes to participate in federated learning; Determining one or more first devices as the candidate members based on the screening information and the first instruction information, wherein the first devices are second devices that wish to participate in federated learning and satisfy the screening information.

[0141] Optionally, the processing module 12 acquiring, from a third network element, information indicating that the first device intends to participate in the federated learning; Based on the intention information and the screening information, a first device that wishes to participate in the federated learning and matches the screening information is determined as a candidate member that can participate in the federated learning.

[0142] Optionally, the processing module 12 Acquiring capability information of federated learning of the first device, the capability information including at least one of a radio access method participating in federated learning, an area participating in federated learning, a time period participating in federated learning, algorithm information that can support participation in federated learning, accuracy information that can be achieved by participation in federated learning, and a type participating in federated learning; Based on the capability information and the screening information, candidate members who can participate in federated learning are determined from the first device, and the capability information of the candidate members is used to match the screening information.

[0143] Here, the ability information of the candidate member matches the screening information, The wireless access method in which the candidate member participates in federated learning is the same as the wireless access method included in the screening information; and The area in which the candidate member will participate in the federated learning is located within the AOI included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and The algorithm types that can support the candidate member's participation in federated learning are included in the algorithm types included in the screening information; The accuracy information that can be achieved by the candidate member participating in the federated learning is higher than the accuracy threshold included in the screening information; and the type of federated learning in which the candidate member participates is the same as the type of federated learning included in the screening information.

[0144] Optionally, the processing module 12 obtaining network status information corresponding to the first device, the network status information comprising: type information of the first device; location information of the first device; wireless access method information in which the first device is located; and wireless signal quality information of the first device; Based on the network status information corresponding to the first device and the screening information, candidate members that can participate in federated learning from the first device are determined, and the network status information corresponding to the candidate members is used to match the screening information.

[0145] Here, the network status information corresponding to the candidate member matches the screening information, The wireless access system in which the candidate member is located is the same as the wireless access system included in the screening information; the candidate member's wireless signal quality information is greater than the signal quality requirement included in the screening information; and The position of the candidate member is located within an AOI included in the screening information; and the type of the candidate member is included in member type information included in the screening information.

[0146] Optionally, the processing module: The method includes: obtaining capability information for each second device to participate in federated learning, the capability information including at least one of the radio access method to participate in federated learning, the area to participate in federated learning, the time to participate in federated learning, algorithm information that can support participation in federated learning, accuracy information that can be achieved in participation in federated learning, and the type to participate in federated learning; and determining one or more first devices as candidate members based on the screening information, the first instruction information, and the capability information, wherein the first device is a second device that wishes to participate in federated learning and whose federated learning capability information satisfies the screening information.

[0147] Here, the ability information to participate in the federated learning satisfies the screening information. The wireless access method of the candidate member participating in the federated learning is the same as the wireless access method included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and The algorithm types that can support the candidate member's participation in federated learning are included in the algorithm types included in the screening information; The accuracy information that can be achieved by the candidate member participating in the federated learning is higher than the accuracy threshold included in the screening information; and a type of the candidate member to participate in federated learning is the same as the type of federated learning included in the screening information. Optionally, the request message further includes second instruction information, the second instruction information being used to indicate a service type corresponding to the federated learning; The processing module includes: used to determine one or more first devices as the candidate members based on the screening information and the second instruction information; Here, the first device is a device that can support a service corresponding to the service type.

[0148] Optionally, the request message further includes sorting instruction information, which is used to instruct sorting the candidate members according to second information, and the second information includes at least one of signal quality of the candidate members, accuracy information of the candidate members, and traffic information of the candidate members; The order of the identifiers of the candidate members in the response message is the order obtained after sorting according to the second information.

[0149] Optionally, the request message further includes packet indication information, and the packet indication information is used to instruct the candidate member to packetize according to third information, and the third information is: the candidate member's location area, the time period when the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access scheme of the candidate member, and at least one of the signal quality of the candidate member; The identifier of the candidate member in the response message is the identifier obtained after packetizing according to the third information.

[0150] Optionally, the response message further includes at least one of the area where the candidate member is located, the time period during which the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access method of the candidate member, the signal quality of the candidate member, the traffic information of the candidate member, and the time period during which the candidate member is able to perform federated learning.

[0151] The device of this embodiment can be used to implement the method of any one of the embodiments of the method on the first network element side described above, and its specific implementation process and technical effect are similar to those of the embodiment of the method on the first network element side, and specific reference may be made to the detailed introduction in the embodiment of the method on the first network element side, and no further description will be given here.

[0152] 6 is a second structural diagram of the candidate member determination device according to the present application. As shown in FIG. 6, the candidate member determination device according to this embodiment includes: a sending module 21 for sending a request message to a first network element, the request message including screening information, the request message being used to instruct the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information; a receiving module (22) for receiving a response message sent by the first network element, the response message including an identifier of the candidate member; wherein the screening information is a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0153] Optionally, the first device comprises: a first device that is in a network-connected state during the time period; a first device that supports performing federated learning during the time period; a first device that supports the algorithm type; a first device that supports federated learning using the algorithm type; A first device whose accuracy information of model training is greater than the accuracy threshold; a first device in the wireless access system; a first device that supports federated learning in the wireless access scheme; a first device whose signal quality is greater than a signal quality requirement; a first device whose consumed traffic is in the traffic range; a first device that meets the type requirements of the candidate member; a first device located within the AOI; and a first device whose federated learning type is the same as the federated learning type information included in the screening information. Optionally, the request message further includes instruction information, which is used to instruct the execution of a task to determine candidate members to participate in the federated learning, or to indicate that the current task is for federated learning or for selecting members for federated learning.

[0154] Optionally, the request message further includes sorting instruction information, which is used to instruct sorting the candidate members according to second information, and the second information includes at least one of signal quality of the candidate members, accuracy information of the candidate members, and traffic information of the candidate members; The identifiers of the candidate members in the response message are the identifiers obtained after sorting according to the second information.

[0155] Optionally, the request message further includes packet indication information, which is used to instruct the candidate member to packetize according to third information, and the third information includes at least one of the following: an area where the candidate member is located; a time period when the candidate member is in a network connection state; an algorithm type supported by the candidate member; accuracy information of the candidate member; a radio access scheme of the candidate member; and a signal quality of the candidate member; The identifier of the candidate member in the response message is the identifier obtained after packetizing according to the third information.

[0156] Optionally, the response message further includes the area where the candidate member is located, the time period during which the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access method of the candidate member, the signal quality of the candidate member, traffic information of the candidate member, and the time period during which the candidate member is able to perform federated learning.

[0157] Optionally, the apparatus further comprises a processing module 23; The processing module 23 is used to determine target members to participate in federated learning based on the identifiers of the candidate members.

[0158] The device of this embodiment can be used to implement the method of any one of the embodiments of the method on the second network element side described above, and its specific implementation process and technical effect are similar to those of the embodiments of the method on the second network element side, and specific reference may be made to the detailed introduction in the embodiments of the method on the second network element side, and no further description will be given here.

[0159] In the embodiments of the present application, the candidate member determination device may be an electronic device, such as an electronic device having an operating system, or a component of an electronic device, such as an integrated circuit or chip. The electronic device may be a terminal or other device other than a terminal. Exemplarily, the terminal may include, but is not limited to, the types of terminals 11 listed above. The other device may be a server, a network-attached storage (NAS), etc., and the embodiments of the present application are not specifically limited thereto.

[0160] The candidate member determination device according to the embodiment of the present application can realize each process realized by the method embodiment of Figures 2 to 4 and achieve the same technical effects, and will not be further described here to avoid repetition of description.

[0161] Optionally, as shown in Fig. 7, an embodiment of the present application further provides a communication device 700, which includes a processor 701 and a memory 702, and the memory 702 stores a program or instruction that can run on the processor 701. For example, if the communication device 700 is a terminal, when the program or instruction is executed by the processor 701, it can realize each step of the embodiment of the method for determining a candidate member, and achieve the same technical effect. If the communication device 700 is a network-side device, when the program or instruction is executed by the processor 701, it can realize each step of the embodiment of the method for determining a candidate member, and achieve the same technical effect, and in order to avoid repetition, it will not be further described here.

[0162] An embodiment of the present application further provides a first network element, the first network element including a processor and a communication interface, the processor being used to determine one or more first devices as candidate members capable of participating in federated learning based on screening information, the communication interface being used to receive a request message sent by a second network element and send a response message to the second network element, the response message including identifiers of the candidate members. The request message includes screening information, the screening information including at least one of a time period, an algorithm type, an accuracy threshold, a radio access method, a signal quality requirement, a traffic range, member type information, number information, area information, and federated learning type information. This terminal embodiment corresponds to the above-mentioned terminal-side method embodiment, and the implementation processes and implementation manners of the above-mentioned method embodiment can be applied to this terminal embodiment, and the same technical effects can be achieved. Specifically, FIG. 8 is a schematic diagram of a hardware structure for implementing the first network element of the embodiment of the present application.

[0163] The first network element 800 includes at least some components such as, but not limited to, a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, and a processor 810.

[0164] As can be understood by those skilled in the art, the first network element 800 may further include a power source (e.g., a battery) for powering each component, and the power source may be logically connected to the processor 810 by a power management system, thereby enabling the power management system to realize functions such as charge / discharge management and power consumption management. The first network element structure shown in Figure 8 does not constitute a limitation on the first network element, and the first network element may include more or fewer components than the number of components shown, or a combination of some components, or a different arrangement of components, which will not be further described here.

[0165] It should be understood that in the embodiment of the present application, the input unit 804 is a graphics Processor The display unit 806 may include a graphics processing unit (GPU) 8041 and a microphone 8042. The graphics processor 8041 processes image data of still or video images captured by an image capture device (e.g., a camera) in a video capture mode or an image capture mode. The display unit 806 may include a display panel 8061, which may be configured in the form of a liquid crystal display (LCD), an organic light emitting diode (OLED), or the like. The user input unit 807 includes at least one of a touch panel 8071 and other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. The other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control buttons, switch buttons, etc.), a trackball, a mouse, and a control lever, which will not be described further herein.

[0166] In the embodiment of the present application, the radio frequency unit 801 can receive downlink data from the network side device and then transmit the data to the processor 810 for processing, and can also transmit uplink data to the network side device. Generally, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0167] The memory 809 may be used to store software programs or instructions and various data. The memory 809 may include a first storage area that mainly stores programs or instructions and a second storage area that stores data. Here, the first storage area may store an operating system, an application program or instructions necessary for at least one function (e.g., an audio playback function, an image playback function, etc.), etc. The memory 809 may include volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Here, the nonvolatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct Rambus random access memory (DRRAM). 8 09 may include, but is not limited to, these and any other suitable types of memory.

[0168] The processor 810 may include one or more processing units. Optionally, the processor 810 integrates an application processor and a modem processor. Here, the application processor mainly processes operations related to the operating system, user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. As can be understood, the modem processor does not have to be integrated into the processor 810.

[0169] Here, the radio frequency unit 801 is used to receive a request message sent by a second network element, the request message including screening information, and send a response message to the second network element, the response message including an identifier of the candidate member.

[0170] The processor 810 is used to determine one or more first devices as candidate members that can participate in federated learning based on the screening information, the screening information comprising: a time period for indicating a selective time period during which the associative learning is performed; an algorithm type for indicating a model training algorithm type that needs to be supported to perform the federated learning; an accuracy threshold for indicating model training accuracy requirements that must be met to perform the federated learning; a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; a signal quality requirement for indicating a wireless signal quality requirement when performing the joint learning; a traffic range for indicating the traffic usage range requirements of the potential member; Member type information for indicating type requirements of candidate members participating in federated learning; Numerical information for indicating the number of candidate members required; an area of ​​interest (AOI) for indicating an area where the candidate members are located; and type information of the federated learning for indicating whether the federated learning belongs to vertical federation or horizontal federation.

[0171] In the above embodiment, the first network element receives a request message sent by the second network element and determines one or more first devices as candidate members capable of participating in federated learning based on screening information included in the request message, and sends identifier information of the candidate members to the second network element. The screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, an area of ​​interest (AOI), and a type of federated learning. The first network element can improve the training efficiency of federated learning by screening suitable participating members for federated learning based on the screening information.

[0172] Optionally, the first device comprises: a first device that is in a network-connected state during the time period; a first device that supports performing federated learning during the time period; a first device that supports the algorithm type; a first device that supports federated learning using the algorithm type; A first device whose accuracy information of model training is greater than the accuracy threshold; a first device in the wireless access system; a first device that supports federated learning in the wireless access scheme; a first device whose signal quality is greater than a signal quality requirement; a first device whose consumed traffic is in the traffic range; a first device that meets the type requirements of the candidate member; a first device located within the AOI; and a first device whose type of associative learning is the same as the type information of associative learning included in the screening information.

[0173] Optionally, the processor 810 further determines a data type corresponding to the screening information; acquiring attribute information corresponding to each of the at least one second device from the at least one third network element based on the data type; The candidate members are determined based on the attribute information and the screening information.

[0174] Optionally, the data type is: The information includes at least one of network connection information and corresponding time period, algorithm type, accuracy information, radio access method, signal quality, and traffic.

[0175] Optionally, the processor 810 further acquires, from the third network element, first indication information for each second device to indicate its willingness to participate in federated learning, the willingness information indicating whether each second device wishes to participate in federated learning; Based on the screening information and the first instruction information, one or more first devices are determined as the candidate members, and the first devices are used to identify second devices that wish to participate in federated learning and that satisfy the screening information.

[0176] Optionally, the processor 810 is used to obtain from a third network element information on the first device's intention to participate in the federated learning, and based on the intention information and the screening information, determine a first device that wishes to participate in the federated learning and matches the screening information as a candidate member that can participate in the federated learning.

[0177] Optionally, processor 810 is used to obtain capability information for federated learning of the first device, wherein the capability information includes at least one of the radio access method participating in federated learning, the area participating in federated learning, the time participating in federated learning, algorithm information that can be supported for participation in federated learning, accuracy information that can be achieved for participation in federated learning, and the type of participation in federated learning, and to determine candidate members that can participate in federated learning from the first device based on the capability information and the screening information, wherein the capability information of the candidate members is matched to the screening information.

[0178] Here, the ability information of the candidate member matches the screening information, The wireless access method in which the candidate member participates in federated learning is the same as the wireless access method included in the screening information; and The area in which the candidate member will participate in the federated learning is located within the AOI included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and The algorithm types that can support the candidate member's participation in federated learning are included in the algorithm types included in the screening information; The accuracy information that can be achieved by the candidate member participating in the federated learning is higher than the accuracy threshold included in the screening information; and the type of federated learning in which the candidate member participates is the same as the type of federated learning included in the screening information.

[0179] Optionally, processor 810 obtains network status information corresponding to the first device, wherein the network status information comprises: type information of the first device; location information of the first device; wireless access method information in which the first device is located; and wireless signal quality information of the first device; Based on the network status information corresponding to the first device and the screening information, candidate members that can participate in federated learning from the first device are determined, and the network status information corresponding to the candidate members is used to match the screening information.

[0180] Here, the network status information corresponding to the candidate member matches the screening information, The wireless access system in which the candidate member is located is the same as the wireless access system included in the screening information; the candidate member's wireless signal quality information is greater than the signal quality requirement included in the screening information; and The position of the candidate member is located within an AOI included in the screening information; and the type of the candidate member is included in member type information included in the screening information.

[0181] Optionally, processor 810 is used to obtain capability information for each second device to participate in federated learning, wherein the capability information includes at least one of the radio access method to participate in federated learning, the area to participate in federated learning, the time to participate in federated learning, algorithm information that can be supported for participation in federated learning, accuracy information that can be achieved for participation in federated learning, and the type to participate in federated learning, and to determine one or more first devices as the candidate members based on the screening information, the first instruction information, and the capability information, wherein the first device is a second device that wishes to participate in federated learning and whose federated learning capability information satisfies the screening information.

[0182] Here, the ability information to participate in the federated learning satisfies the screening information. The wireless access method of the candidate member participating in the federated learning is the same as the wireless access method included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and The algorithm types that can support the candidate member's participation in federated learning are included in the algorithm types included in the screening information; The accuracy information that can be achieved by the candidate member participating in the federated learning is higher than the accuracy threshold included in the screening information; The type of federated learning in which the candidate member participates is the same as the type of federated learning included in the screening information.

[0183] Optionally, the request message further includes second indication information, and the second indication information is used to indicate a service type corresponding to federated learning; The processor 810 is further adapted to determine one or more first devices as the candidate members based on the screening information and the second indication information; Here, the first device is a device that can support a service corresponding to the service type.

[0184] Optionally, the request message further includes sorting instruction information, which is used to instruct sorting the candidate members according to second information, and the second information includes at least one of signal quality of the candidate members, accuracy information of the candidate members, and traffic information of the candidate members; The order of the identifiers of the candidate members in the response message is the order obtained after sorting according to the second information.

[0185] Optionally, the request message further includes packet indication information, and the packet indication information is used to instruct the candidate member to packetize according to third information, and the third information is: the candidate member's location area, the time period when the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access scheme of the candidate member, and at least one of the signal quality of the candidate member; The identifier of the candidate member in the response message is the identifier obtained after packetizing according to the third information.

[0186] Optionally, the response message further includes at least one of the area where the candidate member is located, the time period during which the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access method of the candidate member, the signal quality of the candidate member, the traffic information of the candidate member, and the time period during which the candidate member is able to perform federated learning.

[0187] In the above embodiment, the first network element receives a request message sent by the second network element and determines one or more first devices as candidate members capable of participating in federated learning based on screening information included in the request message, and sends identifier information of the candidate members to the second network element. The screening information includes at least one of a time period, an algorithm type, an accuracy threshold, a wireless access method, a signal quality requirement, a traffic range, member type information, number information, an area of ​​interest (AOI), and a type of federated learning. The first network element can improve the training efficiency of federated learning by screening suitable participating members for federated learning based on the screening information.

[0188] Specifically, an embodiment of the present application further provides a second network element 900. As shown in Fig. 9, the second network element 900 includes a processor 901, a network interface 902, and a memory 903. Here, the network interface 902 is, for example, a common public radio interface (CPRI).

[0189] Specifically, the network side device 900 of the embodiment of the present application further includes instructions or programs stored in memory 903 and capable of running on the processor 901, and the processor 901 calls the instructions or programs in the memory 903 to execute the methods performed by each module shown in FIG. 6, and achieves the same technical effect, which will not be further described here to avoid repetition.

[0190] An embodiment of the present application further provides a readable storage medium, which stores a program or instruction, and when the program or instruction is executed by a processor, it can realize each process of the embodiment of the method for determining candidate members described above and achieve the same technical effect, and in order to avoid repetition of description, it will not be further described here.

[0191] Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0192] An embodiment of the present application further provides a chip, the chip including a processor and a communication interface, the communication interface coupled to the processor, the processor running a program or instruction, used to realize each process of the embodiment of the method for determining candidate members, and can achieve the same technical effect, and in order to avoid repetition of description, it will not be further described here.

[0193] It should be understood that the chips referred to in the embodiments of this application may be referred to as system level chips, system chips, chip systems, or system-on-chips.

[0194] The embodiments of the present application further provide a computer program / program product, which is stored in a storage medium, and which can be executed by at least one processor to realize each process of the embodiments of the above-mentioned candidate member determination method and achieve the same technical effects, and will not be further described here to avoid repetition.

[0195] An embodiment of the present application further provides a candidate member determination system, which includes a first network element and a second network element, wherein the first network element may be used to perform steps of the candidate member determination method described above, and the second network element may be used to perform steps of the candidate member determination method described above.

[0196] It should be noted that, in this specification, the terms "comprises," "including," and any other variations thereof are intended to cover the non-exclusive "comprises," whereby a process, method, article, or apparatus comprising a set of elements not only includes those elements but also other elements not expressly listed or inherent in such process, method, article, or apparatus. Absent further limitations, an element limited by the phrase "comprises one of," does not preclude the presence of other identical elements in the process, method, article, or apparatus comprising that element. It should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may include performing functions in an essentially simultaneous manner or in the reverse order based on the functions involved. For example, the described method may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with reference to some examples may be combined in other examples.

[0197] As will be apparent to those skilled in the art from the above description of the embodiments, the methods of the above embodiments can be realized in the form of software and a necessary general-purpose hardware platform. Of course, they can also be realized in hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical proposal of the present application, in substance or in part contributing to the prior art, may be embodied in the form of a computer software product, which is stored in a storage medium (e.g., ROM / RAM, magnetic disk, optical disk) and includes a number of instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, network device, etc.) to execute the methods described in each embodiment of the present application.

[0198] Although the embodiments of the present application have been described above in conjunction with the drawings, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not limiting. Those skilled in the art can take the teachings of the present application into account and implement many forms without departing from the spirit and scope of the claims, all of which fall within the scope of protection of the present application.

Claims

1. A method for determining candidate members, comprising: A first network element receives a request message sent by a second network element, the request message including screening information, and the first network element is an NWDAF; The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information; sending a response message from the first network element to the second network element, the response message including an identifier of the candidate member; The screening information comprises: a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; an area of ​​interest (AOI) for indicating an area in which the candidate member is located; A method for determining candidate members, the method including at least one of the following:

2. The screening information is Further comprising a time period for indicating a selective time period during which the associative learning is performed; The first device is a first device that is in a network-connected state during the time period; a first device that supports performing federated learning during the time period; a first device in the wireless access system; a first device that supports federated learning in the wireless access scheme; a first device located within the area of ​​interest AOI; The method for determining candidate members according to claim 1, comprising at least one of the following:

3. The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information, The first network element acquires, from a third network element, first instruction information for each second device to indicate its willingness to participate in federated learning, the willingness information indicating whether each second device wishes to participate in federated learning; The method for determining candidate members according to claim 2, further comprising determining one or more first devices as candidate members based on the screening information and the first instruction information, wherein the first devices are devices among the second devices that wish to participate in federated learning and that satisfy the screening information.

4. The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information, The first network element acquires, from a third network element, information indicating that the first device intends to participate in the federated learning; The first network element determines, based on the intention information and the screening information, a first device that wishes to participate in the federated learning and matches the screening information as a candidate member that can participate in the federated learning; Or, The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information, The first network element acquires federated learning capability information of the first device, the capability information including at least one of a radio access method participating in federated learning, an area participating in federated learning, and a time period participating in federated learning; The first network element determines candidate members that can participate in federated learning from the first device based on the capability information and the screening information, where the capability information of the candidate members matches the screening information; Or, The first network element determines one or more first devices as candidate members that can participate in federated learning based on the screening information, The first network element obtains network status information corresponding to the first device, wherein the network status information comprises: location information of the first device; and wireless access method information in which the first device is located; The first network element determines candidate members that can participate in federated learning from the first device based on network status information corresponding to the first device and the screening information, wherein the network status information corresponding to the candidate members matches the screening information; The method for determining candidate members according to claim 1 .

5. The screening information is Further comprising a time period for indicating a selective time period during which the associative learning is performed; The ability information of the candidate member matches the screening information, The wireless access method in which the candidate member participates in federated learning is the same as the wireless access method included in the screening information; and The area in which the candidate member will participate in federated learning is located within the AOI included in the screening information; The time when the candidate member will participate in the federated learning is within the time period included in the screening information; and and Or, The network status information corresponding to the candidate member matches the screening information, The wireless access system in which the candidate member is located is the same as the wireless access system included in the screening information; the location of the candidate member is located within an AOI included in the screening information; The method for determining candidate members according to claim 4, comprising at least one of the following:

6. determining one or more first devices as the candidate members based on the screening information and the first indication information; The first network element acquires capability information of each second device participating in federated learning, the capability information including at least one of a radio access method participating in federated learning, an area participating in federated learning, and a time period participating in federated learning; The method for determining candidate members as described in claim 3, comprising determining one or more first devices as candidate members based on the screening information, the first instruction information and the capability information, wherein the first devices are second devices that wish to participate in federated learning and whose capability information for federated learning satisfies the screening information.

7. The screening information is Further comprising a time period for indicating a selective time period during which the associative learning is performed; the request message includes at least one of second instruction information, sort instruction information, and packet instruction information; the second indication information is used to indicate a service type corresponding to federated learning; the sorting instruction information is used to instruct sorting of the candidate members according to second information, the second information including at least one of signal quality of the candidate members, accuracy information of the candidate members, and traffic information of the candidate members; the order of the identifiers of the candidate members in the response message is the order obtained after sorting according to the second information; The packet instruction information is used to instruct the candidate member to packetize according to third information, and the third information includes: the candidate member's location area, the time period when the candidate member is in a network connection state, the algorithm type supported by the candidate member, the accuracy information of the candidate member, the radio access scheme of the candidate member, and at least one of the signal quality of the candidate member; the identifier of the candidate member in the response message is the identifier obtained after packetizing according to the third information; Or, 2. The method for determining a candidate member according to claim 1, wherein the response message further includes at least one of the following: an area in which the candidate member is located; a time period in which the candidate member is in a network connection state; an algorithm type supported by the candidate member; accuracy information of the candidate member; a radio access method of the candidate member; signal quality of the candidate member; traffic information of the candidate member; and a time period in which the candidate member is able to perform federated learning.

8. A method for determining candidate members, comprising: A second network element sends a request message to a first network element, the request message including screening information, the request message is used to instruct the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information, and the first network element is an NWDAF; receiving, by the second network element, a response message sent by the first network element, the response message including an identifier of the candidate member; The screening information comprises: a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; an area of ​​interest (AOI) for indicating an area in which the candidate member is located; A method for determining candidate members, the method including at least one of the following:

9. The screening information is Further comprising a time period for indicating a selective time period during which the associative learning is performed; The first device is a first device that is in a network-connected state during the time period; a first device that supports performing federated learning during the time period; a first device in the wireless access system; a first device that supports federated learning in the wireless access scheme; a first device located within the area of ​​interest AOI; 9. The method for determining candidate members according to claim 8, comprising at least one of the following:

10. A candidate member determination device, applied to a first network element, wherein the first network element is an NWDAF; a receiving module for receiving a request message sent by a second network element, the request message including screening information; a processing module for determining one or more first devices as candidate members capable of participating in federated learning based on the screening information; a sending module for sending a response message to the second network element, the response message including an identifier of the candidate member; The screening information comprises: a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; an area of ​​interest (AOI) for indicating an area in which the candidate member is located; A candidate member determination device including at least one of the following:

11. The screening information is The candidate member determination device according to claim 10, further comprising a time period for indicating a selective time period during which the federated learning is performed.

12. A candidate member determination device, a sending module for sending a request message to a first network element, the request message including screening information, the request message being used to instruct the first network element to determine one or more first devices as candidate members that can participate in federated learning based on the screening information, the first network element being an NWDAF; a receiving module for receiving a response message sent by the first network element, the response message including an identifier of the candidate member; The screening information comprises: a radio access scheme for indicating a radio access scheme that needs to be selected to perform the federated learning; an area of ​​interest (AOI) for indicating an area in which the candidate member is located; and a candidate member determining device including at least one of:

13. The screening information is The candidate member determination device according to claim 12, further comprising a time period for indicating a selective time period during which the federated learning is performed.

14. A first network element comprising a processor and a memory, the memory storing a program or instructions operable on the processor, the program or instructions, when executed by the processor, realizing the method for determining candidate members described in any one of claims 1 to 7.

15. A second network element comprising a processor and a memory, the memory storing a program or instructions operable on the processor, the program or instructions realizing the method for determining candidate members described in claim 8 or 9 when executed by the processor.

16. A readable storage medium having a program or instructions stored thereon, the program or instructions implementing the method for determining candidate members described in any one of claims 1 to 7 when executed by a processor.

17. A readable storage medium having a program or instructions stored thereon, the program or instructions implementing the method for determining candidate members according to claim 8 or 9 when executed by a processor.

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