Communication method, communication node, medium, and program product

Abstract

The invention discloses a communication method, a communication node, a medium and a program product. Comprising the following steps: sending a client information reporting request to at least one second communication node; receiving client information reported by each second communication node in response to the client information reporting request; and determining a federal learning type according to the information of each client, and sending the federal learning type to each second communication node. Through adoption of the technical scheme, in the federal learning preparation process, the federal learning type to be performed is determined based on the received client information, so that the determination mode of the federal learning type which needs to be adopted by each federal learning participant is clarified; and the ability information of each federated learning participant is fully considered in the federated learning type determination process, so that the scene adaptability of the model obtained by training the federated learning type is improved.

Description

Communication methods, communication nodes, media and software products Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method, communication node, medium, and program product. Background Technology

[0002] In the 5th Generation Mobile Communication (5G) system, the Network Data Analytics Function (NWDAF) is a 5G Core Network Function (5GC NF) located in the control plane, which can perform statistical data and machine learning related tasks in the 5G system.

[0003] Current 5G systems support multiple NWDAFs and / or Application Functions (AFs) as participants for federated learning training and / or inference; however, the method for determining the type of federated learning required for each participant remains unclear. Summary of the Invention

[0004] This application provides a communication method, communication node, medium, and program product to solve the problem of unclear federated learning type selection during federated learning. It clarifies the federated learning type required by each participant during the federated learning preparation / training process, thereby improving the scenario adaptability of the model trained through federated learning.

[0005] To achieve the above objectives, embodiments of this application provide a communication method applied to a first communication node, comprising:

[0006] Send a client information reporting request to at least one second communication node;

[0007] Receive client information reported by each second communication node in response to the client information reporting request;

[0008] The federated learning type is determined based on the information from each client, and the federated learning type is sent to each second communication node.

[0009] To achieve the above objectives, embodiments of this application provide a communication method applied to a second communication node, comprising:

[0010] Receive client information reporting requests sent by the first communication node, and report client information to the first communication node;

[0011] Receive the federated learning type sent by the first communication node.

[0012] To achieve the above objectives, embodiments of this application provide a communication method applied to a first communication node, comprising:

[0013] Receive the first piece of information and make a decision based on it;

[0014] The target federated learning type is determined based on the decision result, and the target federated learning type is sent to at least one second communication node.

[0015] To achieve the above objectives, embodiments of this application provide a communication node, including: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing communication between the processor and the memory. When the program is executed by the processor, it implements the steps of the communication method as described in any of the embodiments of this application.

[0016] To achieve the above objectives, embodiments of this application provide a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the communication method of any embodiment of this application.

[0017] To achieve the above objectives, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the communication methods described in the embodiments of this application.

[0018] The communication method, communication node, medium, and program product provided in this application embodiment involve sending a client information reporting request to at least one second communication node; receiving client information reported by each second communication node in response to the client information reporting request; determining the federated learning type based on the client information; and sending the federated learning type to each second communication node. By adopting the above technical solution, during the federated learning preparation process, the type of federated learning to be performed is determined based on the received client information, clarifying the method for determining the federated learning type to be adopted by each federated learning participant. Furthermore, the process of determining the federated learning type fully considers the capability information of each participant, thereby improving the scenario adaptability of the model trained through the federated learning type. Attached Figure Description

[0019] Figure 1 is a schematic diagram of a 5G core network architecture provided in the prior art;

[0020] Figure 2 is a flowchart of a communication method provided in an embodiment of this application;

[0021] Figure 3 is a flowchart of a communication method provided in an embodiment of this application;

[0022] Figure 4 is a flowchart of a communication method provided in an embodiment of this application;

[0023] Figure 5 is a flowchart of a communication method provided in an embodiment of this application;

[0024] Figure 6 is a timing example diagram of federated learning preparation provided by an embodiment of this application;

[0025] Figure 7 is a timing example diagram of a federated learning training provided in an embodiment of this application;

[0026] Figure 8 is a timing example diagram of a federated learning capability reporting provided in an embodiment of this application;

[0027] Figure 9 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0028] Figure 10 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0029] Figure 11 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0030] Figure 12 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0031] Figure 13 is a schematic diagram of the structure of a communication node provided in an embodiment of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be arbitrarily combined with each other.

[0033] The steps illustrated in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in a different order than that presented here.

[0034] The communication method provided in this application can be applied to 5G systems to determine the federated learning type during the preparation or training process of multiple NWDAFs and / or AFs that support federated learning training and / or inference. To clearly describe federated learning in 5G systems, a brief introduction is given here of the 5G core network architecture and the NWDAFs that perform federated learning as 5GC NFs based on the 5G core network architecture.

[0035] Figure 1 is a schematic diagram of a 5G core network architecture provided in the prior art, which has the following functions:

[0036] 1) User Equipment (UE).

[0037] 2) Radio Access Network (RAN). The RAN manages radio resources, transmits user data received through the N3 interface to the UE, and transmits user data from the UE through the N3 interface. The RAN maps between Quality of Service (QoS) traffic in Dedicated Radio Bearer (DRB) and Protocol Data Unit (PDU) sessions.

[0038] 3) Access and Mobility Management Function (AMF). This function includes registration management, connection management, reachability management, and mobility management. It also performs access authentication and authorization. AMF is a Network Attached Storage (NAS) security endpoint used to forward SM NAS data between the UE and the Session Management Function (SMF).

[0039] 4) SMF. This function includes the following: session establishment, modification, and release; UE IP address allocation and management (including optional authorization functions); selection and control of User Plane (UP) functions and downlink data notification, etc. The SMF controls the User Plane Function (UPF) through the N4 interface. The SMF provides the UPF with Packet Detection Rules (PDR) to indicate how to detect user data traffic; provides Forwarding and Routing Control Association Rules (FAR); and provides QoS enforcement rules and Usage Reporting Rules (URR) to indicate how the UPF performs user data traffic forwarding, QoS processing, and usage reporting on user data traffic detected using PDR.

[0040] 5) UPF. This function includes the following: serving as an anchor point for intra / inter-radio access type mobility, packet routing and forwarding, traffic usage reporting, QoS processing for the UP, downlink packet buffering, and downlink data notification triggering. The General Packet Radio Service (GPRS) Tunneling Protocol for User Plane (GTP-U) tunnel is used for the N3 interface between the RAN and UPF. The GTP-U tunnel operates on a per-PDU session basis. For downlink traffic, the UPF binds the downlink traffic to the QoS traffic within the PDU session's GTP-U tunnel using the FAR received from the SMF. For uplink traffic, the RAN transmits user plane traffic to the QoS stream identified by the UE.

[0041] 6) Policy Control Function (PCF). The PCF provides QoS policy rules to control plane functions for enforcement. The PCF translates AF requests into PCC rules applicable to PDU sessions.

[0042] 7) Unified Data Management (UDM). The UDM performs 3GPP AKA authentication credential generation, access authorization based on subscription data, UE service NF registration management (e.g., storing AMF for UE storage services, SMF for UE PDU session storage services), and subscription management. The UDM accesses the UDR to retrieve UE subscription data and stores the UE context in the UDR. The UDM and UDR can be deployed together.

[0043] Based on the 5G core network architecture, the NWDAF is a 5GC NF located in the control plane, performing data statistics and machine learning-related tasks in the 5G system. The NWDAF can interact with different entities for various purposes:

[0044] Data is collected based on event subscriptions provided by AMF, SMF, UPF, PCF, UDM, NSCAF, AF (directly or through NEF) and Operations, Administration and Maintenance (OAM);

[0045] [Optional] Use the Data Collection Coordination Function (DCCF) for analysis and data collection;

[0046] Retrieve information from data repositories (e.g., retrieve UDRs related to users via UDM or retrieve PFD information via NEF (PFDF));

[0047] Collect location information data from the LCS system;

[0048] [Optional] Store and retrieve information from the Analytics Data Storage Function (ADRF);

[0049] [Optional] Analyze and collect data from the Messaging Framework Adaptor Function (MFAF);

[0050] Retrieve information about NF (e.g., retrieve NF-related information from NRF);

[0051] Provide analytics to consumers on demand.

[0052] Provides batch data related to the analysis ID.

[0053] Provides information on the accuracy of the analysis ID.

[0054] Provides information on the accuracy of machine learning (ML) models or indications of ML model accuracy degradation.

[0055] In some examples, a single instance or multiple instances of NWDAF can be deployed in a public terrestrial mobile network. NWDAF may contain the following logical functions:

[0056] Analysis Logic Function (AnLF): A logic function in NWDAF used to perform inference, derive analytical information (i.e., derive statistical data and / or predictions based on analytical consumer requests) and expose analytical services (i.e., Nnwdaf_AnalyticsSubscription or Nnwdaf_AnalyticsInfo).

[0057] Model Training Logic Function (MTLF): A logic function in NWDAF used to train ML models and expose new training services (such as providing pre-trained ML models).

[0058] An NWDAF can contain a Model Training Logical Function (MTLF) or an Analysis Logic Function (AnLF), or both.

[0059] The Data Collection Coordination and Function (DCCF) is also an NF on the 5G core network control plane. The DCCF is responsible for coordinating the collection and distribution of data requested by NF consumers. It prevents data sources from processing multiple subscriptions to the same data and prevents multiple notifications containing the same information from being sent due to incoordination of data consumer requests.

[0060] DCCF is applicable to:

[0061] NWDAF requests data from a data source (such as for computational analysis).

[0062] NF consumers are analyzed from the NWDAF data source.

[0063] An NF consumer that requests data from an ADRF data source.

[0064] ADRF that receives data from NF data sources.

[0065] To clearly describe the application of federated learning in 5G systems, the following is a brief introduction to horizontal federated learning and vertical federated learning:

[0066] Vertical federated learning is suitable for situations where there is little overlap in the data features of participants, but a lot of overlap in sample IDs. In vertical federated learning, the training data for different client models have the same sample space but different feature spaces.

[0067] In the already defined core network vertical federated learning, NWDAF / AF can serve as a vertical federated learning server and / or a vertical federated learning client.

[0068] Horizontal federated learning is suitable for situations where participants' data features overlap significantly, while sample IDs overlap less. In horizontal federated learning, different client models train on data with the same feature space but different sample spaces.

[0069] In the already defined core network horizontal federated learning, only NWDAF can serve as a horizontal federated learning server and / or a horizontal federated learning client.

[0070] However, while current 5G systems support multiple NWDAFs and / or AFs as participants in federated learning and / or inference, they do not explicitly provide a method for selecting the federated learning type during the process, making it impossible to choose the most suitable type based on the capabilities of the participants. To address this issue, embodiments of this application provide a communication method, which can be implemented at a first communication node and / or a second communication node. The first and second communication nodes are respectively nodes acting as federated learning servers and clients among the participants in the 5G system. The first and / or second communication nodes are generally electronic devices with certain computing capabilities. In this embodiment, the first and / or second communication nodes can be NWDAFs and / or AFs. In some possible implementations, the communication method can be implemented by a processor calling computer-executable instructions stored in memory.

[0071] In one exemplary embodiment, Figure 2 is a flowchart of a communication method provided in an embodiment of this application. This method can be applied to the process of preparing for federated learning in a 5G system, where a federated learning type is selected for communication nodes participating in federated learning. This method can be executed by a communication device, which can be implemented by software and / or hardware and integrated on the communication node. This method can be applied to a first communication node.

[0072] As shown in Figure 2, the communication method provided in this embodiment of the application specifically includes the following steps:

[0073] S101, Send a client information reporting request to at least one second communication node.

[0074] In this embodiment, the second communication node can be specifically understood as a communication node that participates in the same federated learning process as the first communication node.

[0075] In this embodiment, the client information reporting request can be specifically understood as a request for information from the communication node that needs to participate in federated learning to request its support capabilities for federated learning.

[0076] In a specific example, when preparing for federated learning, the first communication node sends a client information reporting request to at least one second communication node that it wishes to participate in federated learning, requesting the second communication node to provide feedback on its capabilities for federated learning.

[0077] S102. Receive the client information reported by each second communication node in response to the client information reporting request.

[0078] In this embodiment, client information can be specifically understood as information used to characterize the supported federated learning status or federated learning capability.

[0079] In a specific example, each second communication node that receives a client information reporting request feeds back its corresponding client information to the first communication node, which means that the first communication node can receive the client information reported by each second communication node in response to the client information reporting request.

[0080] S103. Determine the federated learning type based on the information from each client and send the federated learning type to each second communication node.

[0081] In this embodiment, the federated learning type can be specifically understood as a type that is determined based on the client information of each second communication node, and that each second communication node can support and carry out federated learning.

[0082] In some examples, federated learning types may include horizontal federated learning, vertical federated learning, multi-level horizontal federated learning, multi-level vertical federated learning, etc.

[0083] In a specific example, the first communication node determines the most suitable federated learning type based on the federated learning capability information of each second communication node contained in the received client information, and sends the federated learning type to each second communication node.

[0084] The communication method provided in this application involves sending a client information reporting request to at least one second communication node; receiving client information reported by each second communication node in response to the client information reporting request; determining the federated learning type based on the client information; and sending the federated learning type to each second communication node. By adopting the above technical solution, during the federated learning preparation process, the type of federated learning to be performed is determined based on the received client information, clarifying the method for determining the federated learning type to be adopted by each federated learning participant. Furthermore, the process of determining the federated learning type fully considers the capability information of each participant, thereby improving the scenario adaptability of the model trained using the federated learning type.

[0085] In some examples, after the first communication node determines the type of federated learning and, based on the determined type, determines the second communication nodes that will participate in the federated learning, it can store the list of the second communication nodes that need to participate in the federated learning and the client information corresponding to each second communication node in the list.

[0086] In one embodiment, the client information includes at least one of the following:

[0087] Data samples or feature information;

[0088] Privacy protection requirements and security capabilities information;

[0089] Data quality information;

[0090] Computational resource information;

[0091] The time period or duration during which horizontal or vertical federated learning can be conducted.

[0092] In some examples, data sample or feature information may include sample identifiers, feature identifiers, total number of samples, feature dimensions, and time information corresponding to the sample or feature data.

[0093] In some examples, privacy requirements may include whether to support the exchange of models, intermediate training parameters, and intermediate results during federated learning.

[0094] In some examples, security capability information may include the types of encryption algorithms supported by the second communication node and homomorphic encryption capabilities.

[0095] In some examples, data quality information may include missing data information (such as the proportion of missing data values) and outlier information (such as the proportion of outliers).

[0096] In some examples, computing resource information may include computing power information (CPU / GPU specifications, computing power available for federated learning) and storage capacity available for federated learning, etc.

[0097] In one embodiment, the federated learning type is determined based on information from each client, including at least one of the following:

[0098] The federated learning type is determined based on the number of overlapping data features and sample identifiers in the information from each client.

[0099] The federated learning type is determined based on the model exchange support capability and intermediate result transmission capability in the information of each client.

[0100] The type of federated learning is determined based on the homomorphic encryption support capabilities and security requirements in the information of each client.

[0101] The type of federated learning is determined based on the data quality information in each client's information.

[0102] The type of federated learning is determined based on the computing resource information in each client's information.

[0103] The type of federated learning is determined based on the time period or duration in the information of each client that allows for horizontal or vertical federated learning.

[0104] In some examples, if the number of overlapping data features in the client information exceeds a preset data feature threshold, while the number of overlapping sample identifiers is less than or equal to a preset sample identifier threshold, then the federated learning type is determined to be horizontal federated learning.

[0105] In some examples, if the number of overlapping data features in each client's information is less than or equal to a preset data feature threshold, while the number of overlapping sample identifiers exceeds a preset sample identifier threshold, then the federated learning type is determined to be vertical federated learning.

[0106] In some examples, if most of the client information supports model exchange during the federated learning process but not the transfer of intermediate results, then the federated learning type is determined to be horizontal federated learning.

[0107] In some examples, if most of the client information does not support model disclosure during the federated learning process but does support the transfer of intermediate results, then the federated learning type is determined to be vertical federated learning.

[0108] In some examples, if most of the client information supports homomorphic encryption and the security requirements exceed a preset threshold, then the federated learning type is determined to be vertical federated learning; otherwise, the federated learning type is determined to be horizontal federated learning.

[0109] In some examples, the data quality information in each client's information is analyzed. If the number of missing samples or features in each client's information exceeds a preset missing threshold, then no federated learning can be performed.

[0110] In some examples, since horizontal federated learning requires more computing resources, the type of federated learning applicable to each second communication node can be determined based on the computing resource information in each client's information. If no second communication node has sufficient computing resources, then no federated learning can be performed.

[0111] In some examples, the appropriate type of federated learning can be determined based on the time period and duration of horizontal or vertical federated learning as specified in the client information.

[0112] In one embodiment, before sending a client information reporting request to at least one second communication node, the method further includes:

[0113] Send a discovery request to the Network Repository Function (NRF); the discovery request includes horizontal federated learning client capabilities and / or vertical federated learning client capabilities.

[0114] At least one second communication node that receives feedback from the network storage function.

[0115] In this embodiment, the discovery request can be specifically understood as a request to the NRF to request communication nodes that can participate in federated learning.

[0116] In a specific example, during preparation for federated learning, a first communication node may send a discovery request to the NRF to request communication nodes that can participate in federated learning. The NRF then sends feedback to the first communication node indicating at least one second communication node that supports the type of federated learning requested in the discovery request.

[0117] In one embodiment, if the first communication node supports the federated learning type, sending the federated learning type to each of the second communication nodes includes:

[0118] Send requests to participate in federated learning to each of the second communication nodes.

[0119] In this embodiment, the request to participate in federated learning can be specifically understood as a request to the second communication node to participate in federated learning of the type of federated learning determined by the first communication node.

[0120] In a specific example, if the first communication node supports the federated learning type it has determined, it can be considered that the first communication node can participate in the federated learning preparation corresponding to that type. In this case, the first communication node sends a request to each second communication node to participate in the federated learning of that type.

[0121] In one embodiment, if the first communication node does not support the federated learning type, sending the federated learning type to each of the second communication nodes includes:

[0122] A target first communication node that supports federated learning is identified through network storage functionality;

[0123] Send the federated learning type, at least one second communication node information and / or each client information to the target first communication node;

[0124] The target first communication node sends a request to each second communication node to participate in federated learning.

[0125] In a specific example, if the first communication node does not support the determined federated learning type, to ensure that the second communication nodes can prepare for federated learning normally, a new first communication node that supports the federated learning type will be determined through NRF, and this new first communication node will be designated as the target first communication node. The current first communication node will then send the federated learning type, information about at least one previously acquired second communication node, and / or the client information corresponding to each second communication node to the target first communication node. The target first communication node will then send participation requests to each second communication node, requesting them to participate in the federated learning of this type.

[0126] In one embodiment, participating in a federated learning request includes at least one of the following:

[0127] Federated learning type;

[0128] The time period for federal learning;

[0129] The duration of federal learning.

[0130] In one embodiment, after sending the federated learning type to each of the second communication nodes, the method further includes:

[0131] Receive instructions from each second communication node to join the federated learning.

[0132] In this embodiment, the joining federated learning instruction can be specifically understood as an instruction used to indicate whether the second communication node that receives the request to participate in federated learning should join the federated learning of this type.

[0133] In a specific example, after the first communication node sends the federated learning type to each of the second communication nodes, each of the second communication nodes will determine whether to join the federated learning of that type based on its own federated learning capabilities, and will then send a feedback instruction to the first communication node to join the federated learning based on the determined result.

[0134] In one embodiment, the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

[0135] In one exemplary embodiment, Figure 3 is a flowchart of a communication method provided in an embodiment of this application. This method can be applied to the process of preparing for federated learning in a 5G system, where a federated learning type is selected for communication nodes participating in federated learning. This method can be executed by a communication device, which can be implemented by software and / or hardware and integrated on the communication node. This method can be applied to a second communication node.

[0136] As shown in Figure 3, the communication method provided in this embodiment of the application specifically includes the following steps:

[0137] S201. Receive the client information reporting request sent by the first communication node, and report the client information to the first communication node.

[0138] In a specific example, after receiving a client information reporting request sent by the first communication node, the second communication node will generate client information based on the information requested in the client information reporting request and report the client information to the first communication node.

[0139] S202, Receive the federated learning type sent by the first communication node.

[0140] In a specific example, after reporting client information, the second communication node will wait for the first communication node to make a decision based on the client information reported by each second communication node, and receive the federated learning type finally determined by the first communication node.

[0141] In one embodiment, the client information includes at least one of the following:

[0142] Data samples or feature information;

[0143] Privacy protection requirements and security capabilities information;

[0144] Data quality information;

[0145] Computational resource information;

[0146] The time period or duration during which horizontal or vertical federated learning can be conducted.

[0147] In one embodiment, receiving the federated learning type sent by the first communication node includes:

[0148] Receive the request to participate in federated learning sent by the first communication node.

[0149] In one embodiment, participating in a federated learning request includes at least one of the following:

[0150] The type of federated learning;

[0151] The time period for federal learning;

[0152] The duration of federal learning.

[0153] In one embodiment, after receiving the federated learning type sent by the first communication node, the method further includes:

[0154] Send a message to the first communication node instructing it to join the federated learning process.

[0155] In a specific example, after receiving the federated learning type sent by the first communication node, the second communication node will determine whether to join the federated learning of that type based on its own federated learning capabilities, and will feed back the result of whether to join to the first communication node through a join federated learning instruction.

[0156] In one embodiment, the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

[0157] In one exemplary embodiment, Figure 4 is a flowchart of a communication method provided in an embodiment of this application. This method can be applied to the situation where, during the federated learning training process of a 5G system, a federated learning type is selected for the communication nodes participating in the federated learning. This method can be executed by a communication device, which can be implemented by software and / or hardware and integrated on the communication node. This method can be applied to a first communication node.

[0158] As shown in Figure 4, the communication method provided in this embodiment of the application specifically includes the following steps:

[0159] S301. Receive the first information and make a decision based on the first information.

[0160] In this embodiment, the first information can be specifically understood as information used to characterize the state of federated learning, or information on changes in the state of the second communication node participating in federated learning.

[0161] In this embodiment, the second communication node can be specifically understood as a communication node that participates in the same federated learning as the first communication node and whose federated learning state can be adjusted by the first communication node.

[0162] In a specific example, the first communication node receives first information to characterize the state of federated learning or the state changes of communication nodes participating in federated learning, and determines, based on the first information, whether each communication node participating in federated learning needs to be adjusted, and obtains a decision result including whether to switch the federated learning type and the required switch to the federated learning type.

[0163] S302. Determine the target federated learning type based on the decision result, and send the target federated learning type to at least one second communication node.

[0164] In this embodiment, the target federated learning type needs to be switched to.

[0165] In a specific example, after the first communication node makes a decision on the first information based on decision logic and obtains the target federated learning type to which it needs to switch, it will send the target federated learning type to at least one second communication node so that each second communication node switches its federated learning to the target federated learning type.

[0166] The communication method provided in this application receives first information and makes a decision based on the first information; determines the target federated learning type based on the decision result, and sends the target federated learning type to at least one second communication node. By adopting the above technical solution, during the federated learning training process, the federated learning capability and state changes of at least one second communication node participating in the training are determined based on the received first information, and then a decision on whether to adjust the federated learning type is made based on the first information. This enables the first communication node to dynamically adjust the federated learning type of the participating federated learning object during the federated learning process, improving the adaptability of federated learning to state changes.

[0167] In one embodiment, the first information is provided by a network storage function and / or at least one second communication node.

[0168] In one embodiment, before receiving the first information, at least one of the following is further included:

[0169] Subscribe to first information from network storage;

[0170] Subscribe to first information from at least one second communication node;

[0171] At least one second communication node periodically sends the first information.

[0172] In a specific example, the first communication node can obtain the first information from the NRF and / or at least one second communication node in different ways. For instance, during the federated learning process, the first communication node can subscribe to federated learning-related information from at least one second communication node participating in the same federated learning process, so that when the federated learning status of at least one second communication node changes, the second communication node can send the first information to the first communication node. Alternatively, during the federated learning process, the first communication node can subscribe to the NRF regarding the capabilities of at least one second communication node participating in the same federated learning process, so that the NRF can send the first information to the first communication node when the capabilities of the second communication node change. Finally, because the first communication node participates in the same federated learning process, at least one second communication node can periodically and proactively send the first information to the first communication node.

[0173] In some examples, when a first communication node subscribes to first information from the NRF, the first communication node may send a subscription message to the NRF, which may include the identifier of the second communication node to be subscribed to.

[0174] In one embodiment, the first information includes at least one of the following:

[0175] Changes in data samples or feature information of the second communication node;

[0176] Changes in local privacy protection requirements for the second communication node;

[0177] Changes in data quality information of the second communication node;

[0178] The second communication node is unable to return the model or intermediate training results within the maximum response time specified by the first communication node;

[0179] The capabilities of the second communication node have changed;

[0180] The second communication node can perform changes in the time period / duration of federated learning.

[0181] In some examples, changes in data samples or feature information may include sample increases, sample decreases, feature increases, and feature decreases.

[0182] In some examples, changes in data quality information may include changes in missing data information (such as changes in the proportion of missing data values) and changes in outlier information (such as changes in the proportion of outliers).

[0183] In some examples, changes in computing resource information may include changes in computing power information (such as changes in CPU / GPU specifications, changes in the amount of computing power available for federated learning) and changes in storage capacity available for federated learning.

[0184] In one embodiment, the decision result includes at least one of the following:

[0185] Switch the federated learning type;

[0186] Switch between federated learning types and model training instructions; the model training instructions include: instructions to use the existing trained model or instructions to resend a new model for training.

[0187] Apply new weights to the intermediate training results / models of each second communication node;

[0188] Terminate or suspend the reasoning and / or training of federated learning;

[0189] Search for a new second communication node to join.

[0190] In a specific example, the first communication node can make decisions based on the federated learning status changes of each second communication node contained in the first information, including whether to switch the type of federated learning, whether to use the original model for training during the switch, whether to apply new weights to each second communication node, whether to terminate or pause federated learning, or whether to find new second communication nodes to join the federated learning.

[0191] In one embodiment, making a decision based on the first information includes at least one of the following:

[0192] Make decisions based on changes in data characteristics and sample identifiers;

[0193] Make decisions based on changes in privacy protection requirements;

[0194] Make decisions based on changes in data quality;

[0195] Make decisions based on changes in computing resources;

[0196] Decisions are made based on changes in federal learning time periods / durations;

[0197] Decisions are made based on the overall optimization of federated learning.

[0198] The following explains the internal logic of the first communication node making a decision based on the first information:

[0199] In one embodiment, making decisions based on changes in data features and sample identifiers includes at least one of the following:

[0200] Decisions are made based on the amount of overlap between data characteristics and sample identifiers;

[0201] Decisions are made based on the distribution changes of data characteristics or sample identifiers.

[0202] In some examples, if the number of overlapping data features is greater than a preset data feature threshold, while the number of overlapping sample identifiers is less than or equal to a preset sample identifier threshold, the decision can be to switch to horizontal federated learning.

[0203] In some examples, if the number of overlapping data features is less than or equal to a preset data feature threshold, but the number of overlapping sample identifiers is greater than a preset sample identifier threshold, the decision can be to switch to vertical federated learning.

[0204] In some examples, if the distribution of data features or sample identifiers changes significantly (e.g., the overlap ratio of sample identifiers increases or decreases significantly due to data updates, and a sample identifier threshold can be set in advance to determine whether a significant change has occurred), the federated learning type can be re-evaluated, and the federated learning type can be switched if the evaluation result is different from the ongoing federated learning type.

[0205] In one embodiment, making decisions based on changes in privacy protection requirements includes at least one of the following:

[0206] Decisions are made based on the model's ability to exchange support and the ability to transfer intermediate training results.

[0207] Decisions are made based on the number of privacy protection requirements that are increasing.

[0208] In some examples, if a majority of the second communication nodes support model exchange during the federated learning process but do not support the transfer of intermediate results, the decision can be made to switch to horizontal federated learning.

[0209] In some examples, if a majority of the second communication nodes do not support model disclosure during the federated learning process but do support the transmission of intermediate results, then the decision can be made to switch to vertical federated learning.

[0210] In some examples, if the majority of privacy requirements in each of the second communication nodes are increased (such as requiring stronger homomorphic encryption), the decision may be to switch to vertical federated learning to reduce the risk of data breaches.

[0211] In one embodiment, making decisions based on changes in data quality includes:

[0212] Make decisions based on the proportion of missing data or outliers.

[0213] In some examples, if the increase in the proportion of missing or outlier data reported by the second communication node exceeds a preset threshold, it may lead to a decrease in training quality. Based on this, the following decisions can be made:

[0214] Adjust the weight of the second communication node to reduce the impact of the second communication node with a high proportion of anomalous data in federated learning;

[0215] Suspend or remove some second communication nodes (if the quality problem of the removed second communication node is serious, the severity can be determined by setting a threshold for the proportion of missing data or outliers);

[0216] If the data quality of all second communication nodes participating in federated learning shows a significant decline exceeding the preset range, a decision can be made to terminate federated learning or switch the federated learning type to adapt to the new data quality characteristics.

[0217] In one embodiment, making decisions based on changes in computing resources includes:

[0218] Decisions are made based on the number of second communication nodes with insufficient computing power reported.

[0219] In some examples, if some of the second communication nodes report insufficient computing power (e.g., reduced CPU / GPU resources), the first communication node can decide to reduce the computational complexity of the training task (e.g., reduce the number of training rounds, reduce the model complexity at the first communication node, etc.). Alternatively, the first communication node can adjust its task allocation strategy to allow second communication nodes with sufficient computing power to undertake more computational tasks.

[0220] In some examples, if the number of reports of insufficient computing power in each of the second communication nodes exceeds a preset threshold, the first communication node may decide to switch to a federated learning type with lower computing resource consumption (such as switching from horizontal federated learning to vertical federated learning).

[0221] In some examples, if the number of no-user communication nodes reporting insufficient computing power exceeds a preset threshold, the first communication node may also decide to suspend federated learning training while waiting for the resources of the second communication node to be restored.

[0222] In one embodiment, making decisions based on changes in federated learning time periods / durations includes at least one of the following:

[0223] Decisions are made based on the number of second communication nodes with shortened available time periods;

[0224] Decisions are made based on the degree to which training needs are met after the available time period is shortened.

[0225] In some examples, where the available time period of some second communication nodes is shortened but training needs can still be met, the first communication node can decide to adjust the training schedule to complete training within an appropriate time window.

[0226] In some examples, if the number of second communication nodes with shortened available time exceeds a preset threshold and cannot meet the training requirements of the current federated learning type, the first communication node may decide to reduce the training cycle or reduce the training tasks, or switch to a federated learning type that is more suitable for short-term training (such as switching from vertical federated learning to horizontal federated learning).

[0227] In one embodiment, making decisions based on the overall optimization of federated learning includes:

[0228] Decisions are made on optimizing overall training efficiency or model accuracy based on the switching results.

[0229] In some examples, if the first communication node discovers that switching the federated learning type can improve the overall training efficiency or model accuracy of federated learning, it can proactively initiate the switching of the federated learning type.

[0230] In one embodiment, if the first communication node supports the target federated learning type, sending the target federated learning type to at least one second communication node includes:

[0231] Send a federated learning switch notification to at least one second communication node.

[0232] In this embodiment, the federated learning switch notification can be specifically understood as a request to the second communication node participating in the current federated learning to switch the federated learning type.

[0233] In a specific example, if the first communication node supports the target federated learning type it has determined, it can be assumed that the first communication node can continue to participate in the federated learning corresponding to the target federated learning type. In this case, the first communication node sends a federated learning switch notification to each of the second communication nodes participating in the current federated learning.

[0234] In one embodiment, the federated learning switch notification includes at least one of the following:

[0235] Target Federated Learning Type;

[0236] Instructions to retain existing trained models;

[0237] A new training model.

[0238] In some examples, the federated learning switch notification may include a target federated learning type to inform the second communication node which federated learning type to switch to; a retention instruction for the original training model to indicate whether the second communication node should retain the original training model if a federated learning type switch is required; and a new training model (which may include a model file or a link containing a model file) distributed to the second communication node if it does not retain the original training model.

[0239] It is important to note that new training model distribution is only possible when switching to the horizontal federated learning type.

[0240] In one embodiment, if the first communication node does not support the target federated learning type, sending the target federated learning type to at least one second communication node includes:

[0241] A target first communication node that supports the target federated learning type is identified through network storage functionality;

[0242] Send the target federated learning type, at least one second communication node information and / or the first information to the target first communication node;

[0243] The target first communication node sends a federated learning switch notification to each of the second communication nodes.

[0244] In a specific example, if the first communication node does not support the determined target federated learning type, in order to enable the second communication nodes to switch federated learning types normally, a new first communication node that supports the target federated learning type will be determined through NRF, and this new first communication node will be designated as the target first communication node. The current first communication node will then send the target federated learning type, at least one previously acquired second communication node information, and / or the first information acquired by each second communication node to the target first communication node. The target first communication node will then send a federated learning switching notification to each second communication node.

[0245] In one embodiment, the federated learning switch notification includes at least one of the following:

[0246] Target Federated Learning Type;

[0247] Instructions to retain existing trained models;

[0248] New training model;

[0249] First communication node switching indication, and address information of the target first communication node;

[0250] Apply new weights to the intermediate training results / models of each second communication node.

[0251] In one embodiment, after sending the target federated learning type to at least one second communication node, the method further includes:

[0252] Receive handover notification responses from each of the second communication nodes.

[0253] In this embodiment, the switching notification response can be specifically understood as response information indicating whether the second communication node has switched to the target federated learning type.

[0254] In a specific example, after the first communication node sends the target federated learning type to each of the second communication nodes, each of the second communication nodes will determine whether it can join the federated learning of the target federated learning type based on its own federated learning capabilities, and will send a switching notification response back to the first communication node based on the determined result.

[0255] In one embodiment, the switching notification response includes an indication of whether the second communication node is performing federated learning.

[0256] In one embodiment, the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

[0257] In one exemplary embodiment, Figure 5 is a flowchart of a communication method provided in an embodiment of this application. This method can be applied to the situation where, during the federated learning training process of a 5G system, a federated learning type is selected for the communication nodes participating in the federated learning. This method can be executed by a communication device, which can be implemented by software and / or hardware and integrated on the communication node. This method can be applied to a second communication node.

[0258] As shown in Figure 5, the communication method provided in this embodiment of the application specifically includes the following steps:

[0259] S401, Report the first information to the first communication node.

[0260] S402, Receive Federated Learning Switching Notification.

[0261] In one embodiment, the first information includes at least one of the following:

[0262] Changes in data samples or feature information of the second communication node;

[0263] Changes in local privacy protection requirements for the second communication node;

[0264] Changes in data quality information of the second communication node;

[0265] The second communication node is unable to return the model or intermediate training results within the maximum response time specified by the first communication node;

[0266] The capabilities of the second communication node have changed;

[0267] The second communication node can perform changes in the time period / duration of federated learning.

[0268] In one embodiment, the federated learning switch notification includes at least one of the following:

[0269] Target Federated Learning Type;

[0270] Instructions to retain existing trained models;

[0271] New training model;

[0272] First communication node switching indication, and address information of the target first communication node;

[0273] Apply new weights to the intermediate training results / models of each second communication node.

[0274] In one embodiment, after receiving the federated learning switch notification, the method further includes:

[0275] Send a handover notification response to the first communication node or the target first communication node;

[0276] The handover notification response includes an indication of whether the second communication node should perform federated learning.

[0277] In one embodiment, the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning, and the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning.

[0278] The communication method of this application is illustrated below through some exemplary schemes. In the following schemes, the federated learning server refers to the first communication node, and the federated learning client refers to the second communication node, as an example for illustration.

[0279] Solution 1: A specific example is provided for selecting the federated learning type for the federated learning client and the federated learning server during the federated learning preparation process. Figure 6 is a timing example diagram of federated learning preparation provided by an embodiment of this application. As shown in Figure 6, the specific steps include:

[0280] 0. The federated learning server (such as NWDAF, AF) receives an analysis retrieval request or a model retrieval request, which includes an analysis identifier (ID).

[0281] In some examples, the analytics fetch request or model fetch request received by the federated learning server is used to trigger the federated learning server to prepare for federated learning.

[0282] 1. The federated learning server sends a discovery request to the NRF to discover federated learning clients.

[0283] In some examples, it was found that the request covered all types of federated learning (such as horizontal federated learning, vertical federated learning, or both).

[0284] 2. NRF returns one or more NWDAFs or AFs as federated learning clients.

[0285] 3. The Federated Learning Server sends a Client Information Reporting Request to the Federated Learning Client, requesting the Federated Learning Client to report client information in the Client Information Reporting Request.

[0286] The client information includes at least one of the following:

[0287] Data sample or feature information (such as sample identifiers, feature identifiers, total number of samples, feature dimensions, and time information corresponding to the sample or feature data);

[0288] Privacy protection requirements (such as whether it supports exchanging models, intermediate training parameters, and intermediate results during federated learning) and security capabilities (such as supported encryption algorithm types and homomorphic encryption capabilities);

[0289] Data quality information (such as missing data information (e.g., the proportion of missing data values) and outlier information (e.g., the proportion of outliers));

[0290] Computing resource information (such as computing power information (CPU / GPU specifications, computing power available for federated learning) and storage capacity available for federated learning);

[0291] The time period or duration during which horizontal or vertical federated learning can be conducted.

[0292] 4. The Federated Learning Client reports client information.

[0293] 5. The federated learning server determines whether to conduct federated learning and the type of federated learning (such as horizontal federated learning, vertical federated learning, multi-layer horizontal federated learning, and multi-layer vertical federated learning) based on the client information reported by the federated learning client, the network function type of the federated learning client, and the analysis ID in step 0.

[0294] The federated learning server filters and stores the determined list of federated learning clients and related information (such as the client information reported by the federated learning clients in the federated learning client list in step 4).

[0295] In some examples, the decision logic of a federated learning server may include:

[0296] If the number of overlapping data features in the information of each client exceeds the preset data feature threshold, while the number of overlapping sample identifiers is less than or equal to the preset sample identifier threshold, then the federated learning type is determined to be horizontal federated learning.

[0297] If the number of overlapping data features in each client's information is less than or equal to a preset data feature threshold, while the number of overlapping sample identifiers exceeds a preset sample identifier threshold, then the federated learning type is determined to be vertical federated learning.

[0298] If most of the client information supports model exchange but not intermediate result transfer during the federated learning process, then the federated learning type is determined to be horizontal federated learning.

[0299] If most of the client information does not support model disclosure during the federated learning process but supports intermediate result transmission, then the federated learning type is determined to be vertical federated learning.

[0300] If most of the client information supports homomorphic encryption and the security requirements exceed the preset threshold, then the federated learning type is determined to be vertical federated learning; otherwise, the federated learning type is determined to be horizontal federated learning.

[0301] Analyze the data quality information in each client's information. If the number of missing samples or features in each client's information exceeds the preset missing threshold, then no federated learning can be performed.

[0302] Since horizontal federated learning requires more computing resources, the type of federated learning applicable to each second communication node can be determined based on the computing resource information in each client information. If no second communication node has sufficient computing resources, then no federated learning can be performed.

[0303] The appropriate type of federated learning can be determined based on the time period and duration of horizontal or vertical federated learning that can be performed in each client's information.

[0304] 6a. [If the federated learning server supports the type of federated learning determined in step 5] The federated learning server sends a request to the selected federated learning clients to participate in the federated learning, specifying the type of federated learning, the time period for which the federated learning will take place, and the duration of the federated learning.

[0305] 6b. [If the federated learning server does not support the federated learning type determined in step 5] The source federated learning server discovers a target federated learning server that supports the federated learning type in step 5 through NRF, and sends it the determined federated learning type, a list of federated learning client network functions (including network function IDs or other network function address information), and the relevant information received in step 3, and then executes 6c.

[0306] Among them, the source federated learning server is the federated learning server that does not support the federated learning type in step 5 during the current federated learning process.

[0307] 6c. The target federated learning server sends a join request to the network functions in the federated learning client list, which includes the type of federated learning, the time period for federated learning, and the duration of the federated learning.

[0308] It is understandable that steps 6a and 6b+6c are parallel schemes and will not be executed simultaneously.

[0309] 7. The Federated Learning client sends an instruction to the Federated Learning server whether to join the Federated Learning program.

[0310] Solution 2: A specific example is provided for dynamically selecting the federated learning type for each federated learning client participating in the federated learning training process. Figure 7 is a timing example diagram of a federated learning training provided by an embodiment of this application. As shown in Figure 7, the specific steps include:

[0311] 1. The Federated Learning Server subscribes to the Federated Learning Clients' Federated Learning Capability Information from the NRF.

[0312] The federated learning server can subscribe to the federated learning capabilities information of the federated learning client by sending a subscription message to the NRF. The subscription message may contain the NWDAF ID or AF ID.

[0313] In some examples, reports from the federated learning client can be triggered periodically by the federated learning client or requested by the federated learning server.

[0314] 2. NRF notifies the federated learning server that the federated learning client's federated learning capabilities have changed, or that the time period / duration for horizontal / vertical federated learning has changed.

[0315] 3. The Federated Learning Server subscribes to Federated Learning-related information from the Federated Learning Client.

[0316] 4. The Federated Learning client sends the following information to the Federated Learning server:

[0317] 1) Instructions and reasons for withdrawing from federated learning (Insufficient computing power; federated learning is not supported at this time).

[0318] 2) Changes in data samples or feature information (increase in samples, decrease in samples, increase in features, decrease in features)

[0319] 3) Changes in local privacy protection requirements

[0320] 4) Changes in data quality information (changes in missing data information (e.g., changes in the proportion of missing data values), changes in outlier information (e.g., changes in the proportion of outliers)).

[0321] 5) Changes in computing resource information (changes in computing power information (changes in CPU / GPU specifications, changes in computing power available for federated learning), changes in storage capacity available for federated learning)

[0322] 6) Unable to return intermediate training results within the maximum response time specified by the federated learning server.

[0323] 5. The federated learning server may make the following decisions:

[0324] 1) Switch the federated learning type;

[0325] 2) Switch the federated learning type and the model training instructions; the model training instructions include: instructions to use the existing training model or instructions to resend the new model for training.

[0326] 3) Apply new weights to the intermediate training results / models of each second communication node; where the weights of the intermediate training results correspond to vertical federated learning, and the weights of the models correspond to horizontal federated learning.

[0327] 4) Terminate or suspend the reasoning and / or training of federated learning;

[0328] 5) Find a new second communication node to join.

[0329] The internal decision-making logic of a federated learning server includes at least one of the following:

[0330] Make decisions based on changes in data characteristics and sample identifiers;

[0331] Make decisions based on changes in privacy protection requirements;

[0332] Make decisions based on changes in data quality;

[0333] Make decisions based on changes in computing resources;

[0334] Decisions are made based on changes in federal learning time periods / durations;

[0335] Decisions are made based on the overall optimization of federated learning.

[0336] In some examples, making decisions based on changes in data features and sample identifiers may include:

[0337] If the number of overlapping data features is greater than the preset data feature threshold, while the number of overlapping sample labels is less than or equal to the preset sample label threshold, the decision result can be determined as switching to horizontal federated learning.

[0338] If the number of overlapping data features is less than or equal to the preset data feature threshold, but the number of overlapping sample labels is greater than the preset sample label threshold, the decision result can be determined as switching to vertical federated learning.

[0339] If the distribution of data features or sample identifiers changes significantly (e.g., the overlap ratio of sample identifiers increases or decreases significantly due to data updates, a sample identifier threshold can be set in advance to determine whether a significant change has occurred), the federated learning type can be re-evaluated, and the federated learning type can be switched if the evaluation result is different from the ongoing federated learning type.

[0340] Terminate federal learning;

[0341] Looking for new federated learning clients to join.

[0342] In some examples, making decisions based on changes in privacy protection requirements may include:

[0343] If most federated learning clients support model exchange during the federated learning process but do not support the transfer of intermediate results, then the decision can be made to switch to horizontal federated learning.

[0344] If most federated learning clients do not support model disclosure during the federated learning process but do support the transfer of intermediate results, then the decision can be set to switch to vertical federated learning.

[0345] If most of the privacy protection requirements in various federated learning clients increase (such as requiring stronger homomorphic encryption), the decision may be to switch to vertical federated learning to reduce the risk of data leakage.

[0346] Terminate federal learning;

[0347] Looking for new federated learning clients to join.

[0348] In some examples, making decisions based on changes in data quality may include:

[0349] If the increase in the proportion of missing data or outliers reported by the federated learning client exceeds a preset threshold, it may lead to a decrease in training quality. Based on this, the following decisions can be made:

[0350] Adjust the weights of federated learning clients to reduce the impact of clients with a high proportion of outlier data in federated learning.

[0351] Suspend or remove some federated learning clients (if the quality issues of the removed federated learning clients are serious, the severity of which can be determined by setting a threshold for the proportion of missing data or outliers);

[0352] If the data quality of all federated learning clients participating in federated learning shows a significant decline exceeding the preset range, it may be decided to terminate federated learning or switch the federated learning type to adapt to the new data quality characteristics.

[0353] Looking for new federated learning clients to join.

[0354] In some examples, making decisions based on changes in computing resources may include:

[0355] If some federated learning clients report insufficient computing power (e.g., reduced CPU / GPU resources), the federated learning server can decide to reduce the computational complexity of the training tasks (e.g., reduce training iterations, reduce model complexity at the federated learning server). The federated learning server can also adjust its task allocation strategy to allow federated learning clients with sufficient computing power to undertake more computational tasks.

[0356] If the number of federated learning clients reporting insufficient computing power exceeds a preset threshold, the federated learning server can decide to switch to a federated learning type with lower computing resource consumption (such as switching from horizontal federated learning to vertical federated learning).

[0357] If the number of unattended communication nodes reporting insufficient computing power exceeds a preset threshold, the federated learning server can also decide to suspend federated learning training to wait for the federated learning client's resources to be restored.

[0358] We are looking for new federated learning clients to join in order to supplement computing resources.

[0359] In some examples, making decisions based on changes in the federated learning period / duration may include:

[0360] If the available time period of some federated learning clients is shortened, but the training needs can still be met, the federated learning server can decide to adjust the training schedule to complete the training within the appropriate time window.

[0361] If the number of federated learning clients with shortened available time exceeds a preset threshold and cannot meet the training requirements of the current federated learning type, the federated learning server may decide to reduce the training cycle or reduce the number of training tasks, or switch to a federated learning type that is more suitable for short-term training (such as switching from vertical federated learning to horizontal federated learning).

[0362] In some examples, the overall optimization decision made by federated learning may include:

[0363] If the federated learning server finds that switching the federated learning type can improve the overall training efficiency or model accuracy of federated learning, it can proactively initiate a switch of the federated learning type.

[0364] 6. [If the federated learning server does not support the federated learning type determined in step 5] The source federated learning server discovers a target federated learning server through NRF and submits the federated learning type to be switched, the list of federated learning clients, and the relevant information updated by the federated learning clients in step 3 or the relevant information reported in the federated learning preparation phase to the target federated learning server.

[0365] 7. [If the federated learning server does not support the federated learning type determined in step 5] The source or target federated learning server may send the following information to the federated learning client (including newly discovered federated learning clients):

[0366] 1) Switch to which type of federated learning? If switching, instruct the client whether to retain the existing trained model. If not, distribute the new trained model (model file or a link containing the model file).

[0367] Only by switching to horizontal federated learning can new models be developed.

[0368] 2) Federated learning server switch notification, including the address information of the new federated learning server.

[0369] 3) Apply new weights to the intermediate training results / models of the federated learning client.

[0370] 8. The federated learning client sends a switch notification response to the source federated learning server or the target federated learning server, which includes an instruction on whether to continue federated learning.

[0371] Solution 3: A specific example is provided whereby, before federated learning, an NWDAF / AF, acting as a federated learning client and / or server, reports its federated learning capabilities. Figure 8 is a timing example diagram of federated learning capability reporting provided by an embodiment of this application. As shown in Figure 8, the specific steps may include the following:

[0372] 1. NWDAF and / or AF register or update their federated learning capabilities with NRF, as well as the time periods during which they support various types of federated learning.

[0373] The federated learning capabilities include: whether it supports being used as a horizontal federated learning server, whether it supports being used as a vertical federated learning server, whether it supports being used as a horizontal federated learning client, and whether it supports being used as a vertical federated learning client.

[0374] 2. NRF stores the above capabilities.

[0375] 3. NRF returns a registration or update instruction.

[0376] In one exemplary embodiment, FIG9 is a schematic diagram of a communication device provided in an embodiment of this application. The communication device is applied to a first communication node. As shown in FIG9, the device includes:

[0377] The reporting request sending module 510 is configured to send a client information reporting request to at least one second communication node.

[0378] The client information receiving module 520 is configured to receive client information reported by each second communication node in response to the client information reporting request.

[0379] The first type of sending module 530 is configured to determine the federated learning type based on the information of each client and send the federated learning type to each second communication node.

[0380] In one embodiment, the client information includes at least one of the following:

[0381] Data samples or feature information;

[0382] Privacy protection requirements and security capabilities information;

[0383] Data quality information;

[0384] Computational resource information;

[0385] The time period or duration during which horizontal or vertical federated learning can be conducted.

[0386] In one embodiment, the federated learning type is determined based on information from each client, including at least one of the following:

[0387] The federated learning type is determined based on the number of overlapping data features and sample identifiers in the information from each client.

[0388] The federated learning type is determined based on the model exchange support capability and intermediate result transmission capability in the information of each client.

[0389] The type of federated learning is determined based on the homomorphic encryption support capabilities and security requirements in the information of each client.

[0390] The type of federated learning is determined based on the data quality information in each client's information.

[0391] The type of federated learning is determined based on the computing resource information in each client's information.

[0392] The type of federated learning is determined based on the time period or duration in the information of each client that allows for horizontal or vertical federated learning.

[0393] In one embodiment, before sending a client information reporting request to at least one second communication node, the method further includes:

[0394] Send a discovery request to the network storage function; wherein the discovery request includes horizontal federated learning client capabilities and / or vertical federated learning client capabilities;

[0395] At least one second communication node that receives feedback from the network storage function.

[0396] In one embodiment, if the first communication node supports the federated learning type, sending the federated learning type to each of the second communication nodes includes:

[0397] Send requests to participate in federated learning to each of the second communication nodes.

[0398] In one embodiment, if the first communication node does not support the federated learning type, sending the federated learning type to each of the second communication nodes includes:

[0399] A target first communication node that supports federated learning is identified through network storage functionality;

[0400] Send the federated learning type, at least one second communication node information and / or each client information to the target first communication node;

[0401] The target first communication node sends a request to each second communication node to participate in federated learning.

[0402] In one embodiment, participating in a federated learning request includes at least one of the following:

[0403] Federated learning type;

[0404] The time period for federal learning;

[0405] The duration of federal learning.

[0406] In one embodiment, after sending the federated learning type to each of the second communication nodes, the method further includes:

[0407] Receive instructions from each second communication node to join the federated learning.

[0408] In one embodiment, the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

[0409] In one exemplary embodiment, FIG10 is a schematic diagram of a communication device provided in an embodiment of this application. The communication device is applied to a second communication node. As shown in FIG10, the device includes:

[0410] The first information reporting module 610 is configured to receive client information reporting requests sent by the first communication node and report client information to the first communication node.

[0411] The first type of receiving module 620 is configured to receive federated learning types sent by the first communication node.

[0412] In one embodiment, the client information includes at least one of the following:

[0413] Data samples or feature information;

[0414] Privacy protection requirements and security capabilities information;

[0415] Data quality information;

[0416] Computational resource information;

[0417] The time period or duration during which horizontal or vertical federated learning can be conducted.

[0418] In one embodiment, the first type receiving module 620 is specifically configured to receive a request to participate in federated learning sent by the first communication node.

[0419] In one embodiment, participating in a federated learning request includes at least one of the following:

[0420] Federated learning type;

[0421] The time period for federal learning;

[0422] The duration of federal learning.

[0423] In one embodiment, after receiving the federated learning type sent by the first communication node, the method further includes:

[0424] Send a message to the first communication node instructing it to join the federated learning process.

[0425] In one embodiment, the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

[0426] In one exemplary embodiment, FIG11 is a schematic diagram of a communication device provided in an embodiment of the present application. The communication device is applied to a first communication node. As shown in FIG11, the device includes:

[0427] The decision module 710 is configured to receive the first information and make a decision based on the first information.

[0428] The second type of sending module 720 is configured to determine the target federated learning type based on the decision result and send the target federated learning type to at least one second communication node.

[0429] In one embodiment, the first information is provided by a network storage function and / or at least one second communication node.

[0430] In one embodiment, before receiving the first information, at least one of the following is further included:

[0431] Subscribe to first information from network storage;

[0432] Subscribe to first information from at least one second communication node;

[0433] At least one second communication node periodically sends the first information.

[0434] In one embodiment, the first information includes at least one of the following:

[0435] Changes in data samples or feature information of the second communication node;

[0436] Changes in local privacy protection requirements for the second communication node;

[0437] Changes in data quality information of the second communication node;

[0438] The second communication node is unable to return the model or intermediate training results within the maximum response time specified by the first communication node;

[0439] The capabilities of the second communication node have changed;

[0440] The second communication node can perform changes in the time period / duration of federated learning.

[0441] In one embodiment, the decision result includes at least one of the following:

[0442] Switch the federated learning type;

[0443] Switch between federated learning types and model training instructions; the model training instructions include: instructions to use the existing trained model or instructions to resend a new model for training.

[0444] Apply new weights to the intermediate training results / models of each second communication node;

[0445] Terminate or suspend the reasoning and / or training of federated learning;

[0446] Search for a new second communication node to join.

[0447] In one embodiment, making a decision based on the first information includes at least one of the following:

[0448] Make decisions based on changes in data characteristics and sample identifiers;

[0449] Make decisions based on changes in privacy protection requirements;

[0450] Make decisions based on changes in data quality;

[0451] Make decisions based on changes in computing resources;

[0452] Decisions are made based on changes in federal learning time periods / durations;

[0453] Decisions are made based on the overall optimization of federated learning.

[0454] In one embodiment, making decisions based on changes in data features and sample identifiers includes at least one of the following:

[0455] Decisions are made based on the amount of overlap between data characteristics and sample identifiers;

[0456] Decisions are made based on the distribution changes of data characteristics or sample identifiers.

[0457] In one embodiment, making decisions based on changes in privacy protection requirements includes at least one of the following:

[0458] Decisions are made based on the model's ability to exchange support and the ability to transfer intermediate training results.

[0459] Decisions are made based on the number of privacy protection requirements that are increasing.

[0460] In one embodiment, making decisions based on changes in data quality includes:

[0461] Make decisions based on the proportion of missing data or outliers.

[0462] In one embodiment, making decisions based on changes in computing resources includes:

[0463] Decisions are made based on the number of second communication nodes with insufficient computing power reported.

[0464] In one embodiment, making decisions based on changes in federated learning time periods / durations includes at least one of the following:

[0465] Decisions are made based on the number of second communication nodes with shortened available time periods;

[0466] Decisions are made based on the degree to which training needs are met after the available time period is shortened.

[0467] In one embodiment, making decisions based on the overall optimization of federated learning includes:

[0468] Decisions are made on optimizing overall training efficiency or model accuracy based on the switching results.

[0469] In one embodiment, if the first communication node supports the target federated learning type, sending the target federated learning type to at least one second communication node includes:

[0470] Send a federated learning switch notification to at least one second communication node.

[0471] In one embodiment, the federated learning switch notification includes at least one of the following:

[0472] Target Federated Learning Type;

[0473] Instructions to retain existing trained models;

[0474] A new training model.

[0475] In one embodiment, if the first communication node does not support the target federated learning type, sending the target federated learning type to at least one second communication node includes:

[0476] A target first communication node that supports the target federated learning type is identified through network storage functionality;

[0477] Send the target federated learning type, at least one second communication node information and / or the first information to the target first communication node;

[0478] The target first communication node sends a federated learning switch notification to each of the second communication nodes.

[0479] In one embodiment, the federated learning switch notification includes at least one of the following:

[0480] Target Federated Learning Type;

[0481] Instructions to retain existing trained models;

[0482] New training model;

[0483] First communication node switching indication, and address information of the target first communication node;

[0484] Apply new weights to the intermediate training results / models of each second communication node.

[0485] In one embodiment, after sending the target federated learning type to at least one second communication node, the method further includes:

[0486] Receive handover notification responses from each of the second communication nodes.

[0487] In one embodiment, the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

[0488] In one exemplary embodiment, FIG12 is a schematic diagram of a communication device provided in an embodiment of the present application. The communication device is applied to a second communication node. As shown in FIG12, the device includes:

[0489] The second information reporting module 810 is configured to report the first information to the first communication node.

[0490] The switching notification receiving module 820 is configured to receive federated learning switching notifications.

[0491] In one embodiment, the first information includes at least one of the following:

[0492] Changes in data samples or feature information of the second communication node;

[0493] Changes in local privacy protection requirements for the second communication node;

[0494] Changes in data quality information of the second communication node;

[0495] The second communication node is unable to return the model or intermediate training results within the maximum response time specified by the first communication node;

[0496] The capabilities of the second communication node have changed;

[0497] The second communication node can perform changes in the time period / duration of federated learning.

[0498] In one embodiment, the federated learning switch notification includes at least one of the following:

[0499] Target Federated Learning Type;

[0500] Instructions to retain existing trained models;

[0501] New training model;

[0502] First communication node switching indication, and address information of the target first communication node;

[0503] Apply new weights to the intermediate training results / models of each second communication node.

[0504] In one embodiment, after receiving the federated learning switch notification, the method further includes:

[0505] Send a handover notification response to the first communication node or the target first communication node;

[0506] The handover notification response includes an indication of whether the second communication node should perform federated learning.

[0507] In one embodiment, the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning, and the first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning.

[0508] This application embodiment also provides a communication node. Figure 13 is a structural schematic diagram of a communication node provided in this application embodiment. As shown in Figure 13, the communication node provided in this application embodiment includes a memory 920, a processor 910, and a computer program stored in the memory and executable on the processor. When the processor 910 executes the program, it implements the above-mentioned communication method.

[0509] The communication node may also include a memory 920; the processor 910 in the communication node may be one or more, with one processor 910 as an example in Figure 13; the memory 920 is used to store one or more programs; the one or more programs are executed by the one or more processors 910, so that the one or more processors 910 implement the communication method as described in the embodiments of this application.

[0510] The communication node also includes: a communication device 930, an input device 940, and an output device 950.

[0511] The processor 910, memory 920, communication device 930, input device 940 and output device 950 in the communication node can be connected by a bus or other means. Figure 13 shows an example of connection by bus.

[0512] Input device 940 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the communication node. Output device 950 may include display devices such as a display screen.

[0513] The communication device 930 may include a receiver and a transmitter. The communication device 930 is configured to perform information transmission and reception communication under the control of the processor 910.

[0514] The memory 920, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the communication method described in the embodiments of this application (e.g., a reporting request sending module 510, a client information receiving module 520, a first type sending module 530; or a first information reporting module 610, a first type receiving module 620; or a decision module 710, a second type sending module 720; or a second information reporting module 810, a switching notification receiving module 820). The memory 920 may include a program storage area and a data storage area, wherein the program storage area may store the operating system, an application program required for at least one function, and the data storage area may store data created according to the use of the communication node, etc. In addition, the memory 920 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 920 may further include memory remotely located relative to the processor 910, and these remote memories can be connected to the communication node via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0515] This application also provides a storage medium storing a computer program, which, when executed by a processor, implements any of the communication methods described in this application.

[0516] Optionally, the communication method, applied to a first communication node, includes: sending a client information reporting request to at least one second communication node; receiving client information reported by each second communication node in response to the client information reporting request; determining the federated learning type based on the client information; and sending the federated learning type to each second communication node.

[0517] Optionally, the communication method, applied to the second communication node, includes: receiving a client information reporting request sent by the first communication node and reporting client information to the first communication node; and receiving a federated learning type sent by the first communication node.

[0518] Optionally, the communication method, applied to a first communication node, includes: receiving first information and making a decision based on the first information; determining a target federated learning type based on the decision result, and sending the target federated learning type to at least one second communication node.

[0519] Optionally, this communication method, applied to the second communication node, includes: reporting first information to the first communication node; and receiving a federated learning switch notification.

[0520] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable CD-ROM, optical storage device, magnetic storage device, or any suitable combination thereof. The computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0521] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0522] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, radio frequency (RF), etc., or any suitable combination thereof.

[0523] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0524] Optionally, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the communication method provided in any embodiment of the present invention.

[0525] The above description is merely an exemplary embodiment of this application and is not intended to limit the scope of protection of this application.

[0526] Those skilled in the art will understand that the term user terminal encompasses any suitable type of wireless user equipment, such as mobile phones, portable data processing devices, portable web browsers, or vehicle-mounted mobile stations.

[0527] Generally, the various embodiments of this application can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. For example, some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device, although this application is not limited thereto.

[0528] Embodiments of this application can be implemented by executing computer program instructions through the data processor of a mobile device, for example, in a processor entity, or through hardware, or through a combination of software and hardware. The computer program instructions can be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages.

[0529] Any block diagram of logical flow in the accompanying drawings of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on memory. Memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, read-only memory (ROM), random access memory (RAM), optical storage devices and systems (Digital Video Disc (DVD) or Compact Disk (CD), etc.). Computer-readable media may include non-transitory storage media. Data processors may be of any type suitable to the local technical environment, such as, but not limited to, general-purpose computers, special-purpose computers, microprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and processors based on multi-core processor architectures.

[0530] A detailed description of exemplary embodiments of this application has been provided above through exemplary and non-limiting examples. However, various modifications and adjustments to the above embodiments will be apparent to those skilled in the art when considered in conjunction with the accompanying drawings and claims, without departing from the scope of this application. Therefore, the proper scope of this application will be determined by the claims.

Claims

1. A communication method, characterized in that, The method is applied to a first communication node and includes: sending a client information reporting request to at least one second communication node; receiving client information reported by each of the second communication nodes in response to the client information reporting request; determining a federated learning type based on the client information; and sending the federated learning type to each of the second communication nodes.

2. The communication method according to claim 1, characterized in that, The client information includes at least one of the following: data sample or feature information; privacy protection requirements and security capability information; data quality information; computing resource information; and the time period or duration during which horizontal or vertical federated learning can be performed.

3. The communication method according to claim 1, characterized in that, The determination of the federated learning type based on the client information includes at least one of the following: determining the federated learning type based on the number of overlapping data features and the number of overlapping sample identifiers in the client information; determining the federated learning type based on the model exchange support capability and intermediate result transmission capability in the client information; determining the federated learning type based on the homomorphic encryption support capability and security requirements in the client information; determining the federated learning type based on the data quality information in the client information; determining the federated learning type based on the computing resource information in the client information; or determining the federated learning type based on the time period or duration in the client information where horizontal or vertical federated learning can be performed.

4. The communication method according to claim 1, characterized in that, Before sending the client information reporting request to at least one second communication node, the method further includes: sending a discovery request to the network storage function; wherein the discovery request includes horizontal federated learning client capabilities and / or vertical federated learning client capabilities; and receiving feedback from the at least one second communication node from the network storage function.

5. The communication method according to claim 1, characterized in that, If the first communication node supports the federated learning type, sending the federated learning type to each of the second communication nodes includes sending a request to participate in federated learning to each of the second communication nodes.

6. The communication method according to claim 1, characterized in that, If the first communication node does not support the federated learning type, sending the federated learning type to each of the second communication nodes includes: determining a target first communication node that supports the federated learning type through network storage function; sending the federated learning type, at least one second communication node information and / or each of the client information to the target first communication node; and having the target first communication node send a request to each of the second communication nodes to participate in the federated learning.

7. The communication method according to claim 5 or 6, characterized in that, The request to participate in federated learning includes at least one of the following: the type of federated learning; the time period for which federated learning will take place; and the duration of the federated learning process.

8. The communication method according to claim 1, characterized in that, After sending the federated learning type to each of the second communication nodes, the method further includes: receiving a joining federated learning instruction from each of the second communication nodes.

9. The communication method according to any one of claims 1-6 or 8, characterized in that, The first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

10. A communication method, characterized in that, Applied to the second communication node, it includes: receiving a client information reporting request sent by the first communication node and reporting client information to the first communication node; and receiving a federated learning type sent by the first communication node.

11. The communication method according to claim 10, characterized in that, The client information includes at least one of the following: data sample or feature information; privacy protection requirements and security capability information; data quality information; computing resource information; and the time period or duration during which horizontal or vertical federated learning can be performed.

12. The communication method according to claim 10, characterized in that, The step of receiving the federated learning type sent by the first communication node includes: receiving a request to participate in federated learning sent by the first communication node.

13. The communication method according to claim 12, characterized in that, The request to participate in federated learning includes at least one of the following: the type of federated learning; the time period for which federated learning will take place; and the duration of the federated learning process.

14. The communication method according to claim 10, characterized in that, After receiving the federated learning type sent by the first communication node, the method further includes: sending a feedback instruction to the first communication node to join the federated learning.

15. The communication method according to any one of claims 10-14, characterized in that, The first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

16. A communication method, characterized in that, The method is applied to a first communication node and includes: receiving first information and making a decision based on the first information; determining a target federated learning type based on the decision result and sending the target federated learning type to at least one second communication node.

17. The communication method according to claim 16, characterized in that, The first information is provided by network storage functionality and / or the at least one second communication node.

18. The communication method according to claim 17, characterized in that, Before receiving the first information, the method includes at least one of the following: subscribing to the first information with the network storage function; subscribing to the first information with the at least one second communication node; and the at least one second communication node periodically sending the first information.

19. The communication method according to claim 16, characterized in that, The first information includes at least one of the following: changes in the data samples or feature information of the second communication node; changes in the local privacy protection requirements of the second communication node; changes in the data quality information of the second communication node; the second communication node being unable to return the model or intermediate training results within the longest response time specified by the first communication node; changes in the capabilities of the second communication node; changes in the time period / duration during which the second communication node can perform federated learning.

20. The communication method according to claim 16, characterized in that, The decision result includes at least one of the following: switching the federated learning type; switching the federated learning type and model training instructions; wherein the model training instructions include: instructions to use the original training model or instructions to resend the new model for training; applying new weights to the intermediate training results / models of each of the second communication nodes; terminating or pausing the inference and / or training of the federated learning; and finding a new second communication node to join.

21. The communication method according to claim 16, characterized in that, The decision-making based on the first information includes at least one of the following: making a decision based on changes in data characteristics and sample identifiers; making a decision based on changes in privacy protection requirements; making a decision based on changes in data quality; making a decision based on changes in computing resources; making a decision based on changes in the federated learning period / duration; and making a decision based on the overall optimization of federated learning.

22. The communication method according to claim 21, characterized in that, The decision-making based on changes in data features and sample identifiers includes at least one of the following: making a decision based on the amount of overlap between data features and sample identifiers; or making a decision based on changes in the distribution of data features or sample identifiers.

23. The communication method according to claim 21, characterized in that, The decision-making based on changes in privacy protection requirements includes at least one of the following: making decisions based on the model's ability to exchange support capabilities and the ability to transfer intermediate training results; making decisions based on the amount of increase in privacy protection requirements.

24. The communication method according to claim 21, characterized in that, The decision-making based on changes in data quality includes: making decisions based on the proportion of missing data or the proportion of outliers.

25. The communication method according to claim 21, characterized in that, The decision-making based on changes in computing resources includes: making decisions based on the number of second communication nodes reporting insufficient computing power.

26. The communication method according to claim 21, characterized in that, The decision-making based on changes in the federated learning time period / duration includes at least one of the following: making a decision based on the number of second communication nodes whose available time period has been shortened; or making a decision based on the state of satisfaction of training requirements after the available time period has been shortened.

27. The communication method according to claim 21, characterized in that, The decision-making based on the overall optimization of federated learning includes: making decisions on optimizing the overall training efficiency or model accuracy based on the switching results.

28. The communication method according to claim 16, characterized in that, If the first communication node supports the target federated learning type, sending the target federated learning type to at least one second communication node includes: sending a federated learning switching notification to the at least one second communication node.

29. The communication method according to claim 28, characterized in that, The federated learning switching notification includes at least one of the following: target federated learning type; indication to retain the original training model; new training model.

30. The communication method according to claim 16, characterized in that, If the first communication node does not support the target federated learning type, sending the target federated learning type to at least one second communication node includes: determining a target first communication node that supports the target federated learning type through network storage function; sending the target federated learning type, at least one second communication node information and / or the first information to the target first communication node; and having the target first communication node send a federated learning switching notification to each of the second communication nodes.

31. The communication method according to claim 30, characterized in that, The federated learning switching notification includes at least one of the following: target federated learning type; indication to retain the original training model; new training model; first communication node switching indication, and address information of the target first communication node; and applying new weights to the intermediate training results / models of each second communication node.

32. The communication method according to any one of claims 16-31, characterized in that, After sending the target federated learning type to at least one second communication node, the method further includes: receiving a handover notification response from each of the second communication nodes.

33. The communication method according to claim 32, characterized in that, The switching notification response includes an indication of whether the second communication node should perform federated learning.

34. The communication method according to any one of claims 16-31, characterized in that, The first communication node is a horizontal federated learning server or a vertical federated learning server in federated learning, and the second communication node is a horizontal federated learning client or a vertical federated learning client in federated learning.

35. A communication node, characterized in that, include: The program includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the communication method as described in any one of claims 1-34.

36. A storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the communication method as described in any one of claims 1-34.

37. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the communication method as described in any one of claims 1-34.

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