DEVICE DETERMINATION METHOD, COMMUNICATION DEVICE, AND READABLE STORAGE MEDIUM - Patent application

Abstract

The present application discloses a device determination method, an apparatus, and a communication device. The device determination method includes the steps of: a first communication device transmitting a first request message to a second communication device to request acquisition of network performance analysis information; and the first communication device receiving, from the second communication device, network performance analysis information corresponding to M candidate devices. The network performance analysis information corresponding to one candidate device includes at least one of: radio access method information of the one candidate device; time information of the one candidate device available for participating in federated learning; location information of the one candidate device; time period information covered by the network of the one candidate device; information on a ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest; and network signal quality information of the one candidate device.

Description

[Technical Field]

[0001] The present application relates to the technical field of communications, and in particular to a device determination method, apparatus and communication device. [Background technology]

[0002] With the development of communication technology, federated learning has emerged. Federated learning includes horizontal federated learning and vertical federated learning. Horizontal federated learning increases the number of training samples by combining the same data features of different samples from participating devices, while vertical federated learning increases the feature dimension of training samples by combining the different data features of common samples from participating devices, thereby obtaining a better model.

[0003] "Component selection" is crucial for federated learning. Selecting appropriate devices (e.g., User Equipment (UE)) for federated learning can improve training efficiency. Conversely, selecting inappropriate devices for federated learning can negatively impact training efficiency and results. Take the case of a consumer service device (e.g., an Application Function (AF)) performing federated learning as an example. The AF can typically select several devices in a certain area (e.g., a region or a cell) for federated learning based on the network performance corresponding to that area reported by the Network Data Analytics Function (NWDAF). However, due to differences in network performance corresponding to different devices in the same area, how to select appropriate devices to participate in federated learning is an urgent issue that must be resolved. Summary of the Invention [Problem to be solved by the invention]

[0004] The embodiments of the present application provide a device determination method, an apparatus, and a communication device that can select appropriate devices to participate in federated learning. [Means for solving the problem]

[0005] In a first aspect, a device determination method is provided, comprising: a step in which a first communication device sends a first request message to a second communication device to request acquisition of network performance analysis information; and a step in which the first communication device receives network performance analysis information from the second communication device, wherein the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer, and the network performance analysis information corresponding to one candidate device includes at least one type of information: radio access method information of the one candidate device; time information during which the one candidate device can participate in federated learning; location information of the one candidate device; time period information covered by the network of the one candidate device; information on the ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest; and network signal quality information of the one candidate device.

[0006] In a second aspect, a device determination method is provided, comprising: a step of a second communication device receiving a first request message from a first communication device to request acquisition of network performance analysis information; and a step of the second communication device transmitting network performance analysis information to the first communication device, wherein the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer, and the network performance analysis information corresponding to one candidate device includes at least one type of information: radio access method information of the one candidate device; time information during which the one candidate device can participate in federated learning; location information of the one candidate device; time period information covered by the network of the one candidate device; information on the ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest; and network signal quality information of the one candidate device.

[0007] In a third aspect, a device determination device is provided, the device including: a transmitting module for transmitting a first request message to a second communication device to request the second communication device to obtain network performance analysis information; and a receiving module for receiving network performance analysis information from the second communication device, the network performance analysis information including network performance analysis information corresponding to M candidate devices, where M is a positive integer, and the network performance analysis information corresponding to one candidate device includes at least one type of information: radio access method information of the one candidate device; time information of the one candidate device available to participate in federated learning; location information of the one candidate device; time period information covered by the network of the one candidate device; information on the ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest; and network signal quality information of the one candidate device.

[0008] In a fourth aspect, a device determination device is provided, the device including: a receiving unit for receiving a first request message from a first communication device to request acquisition of network performance analysis information; and a transmitting unit for transmitting the network performance analysis information to the first communication device, the network performance analysis information including network performance analysis information corresponding to M candidate devices, where M is a positive integer, and the network performance analysis information corresponding to one candidate device includes at least one type of information: radio access method information of the one candidate device; time information of the one candidate device available to participate in federated learning; location information of the one candidate device; time period information covered by the network of the one candidate device; information on the ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest; and network signal quality information of the one candidate device.

[0009] In a fifth aspect, there is provided a communications device including a processor and a memory, wherein a program or command executable by the processor is stored in the memory, and when the program or command is executed by the processor, the steps of the device determination method described in the first or second aspect are realized.

[0010] In a sixth aspect, there is provided a communication device including a processor and a communication interface, wherein, when the communication device is a first communication device, the communication interface is for sending a first request message to a second communication device and receiving network performance analysis information from the second communication device, and when the communication device is the second communication device, the communication interface is for receiving the first request message from the first communication device and sending network performance analysis information to the first communication device, the network performance analysis information includes network performance analysis information corresponding to M candidate devices, M is a positive integer, and the network performance analysis information corresponding to one candidate device includes at least one type of information: radio access method information of the one candidate device, time information of the one candidate device available to participate in federated learning, location information of the one candidate device, time period information covered by the network of the one candidate device, information on the ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest, and network signal quality information of the one candidate device.

[0011] In a seventh aspect, there is provided a readable storage medium storing a program or commands that, when executed by a processor, implements the steps of the device determination method according to the first or second aspect.

[0012] In an eighth aspect, there is provided a chip including a processor and a communication interface coupled thereto, the processor being configured to execute a program or command to implement the steps of the device determination method described in the first or second aspect.

[0013] In a ninth aspect, there is provided a computer program / program product that is stored in a storage medium and that, when executed by at least one processor, implements the steps of the device determination method according to the first or second aspect. [Effects of the Invention]

[0014] In an embodiment of the present application, a first communication device sends a first request message to a second communication device to request acquisition of network performance analysis information, and the first communication device receives the network performance analysis information from the second communication device, where the network performance analysis information includes network performances corresponding to M candidate devices, where M is a positive integer. The network performance analysis information corresponding to one candidate device includes at least one of the following information: radio access method information of the one candidate device, time information of the one candidate device available to participate in federated learning, location information of the one candidate device, time period information covered by the network of the one candidate device, information on the ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest, and network signal quality information of the one candidate device. With this technical means, the device's wireless access method information, time information available for participation in federated learning, location information, time period information covered by the network, ratio information of the time period covered by the network to the time of interest, and network signal quality information can all reflect the network performance corresponding to the device, so that when the first communication device receives the network performance analysis information, it can determine the network performance corresponding to each of the M candidate devices, and thereby the first communication device can determine the candidate devices that meet the network performance requirements of federated learning as devices to participate in federated learning, that is, the first communication device can select appropriate devices to participate in federated learning. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic flowchart of a device determination method provided in an embodiment of the present application; [Figure 2] 1 is a schematic diagram of a device determination method provided in an embodiment of the present application. [Figure 3] 2 is a second schematic diagram of a device determination method provided in an embodiment of the present application. [Figure 4]1 is a structural schematic diagram of a device determination apparatus provided in an embodiment of the present application; [Figure 5] 2 is a second structural schematic diagram of a device determination apparatus provided in an embodiment of the present application; [Figure 6] 1 is a structural schematic diagram of a communication device provided in an embodiment of the present application; [Figure 7] FIG. 1 is a hardware schematic diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, the technical solutions in the embodiments of the present application will be clearly explained with reference to the drawings in the embodiments of the present application, and it should be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present application shall fall within the protection scope of the present application.

[0017] In the specification and claims of the embodiments of the present application, technical terms such as "first" and "second" are used to distinguish between different objects, not to describe a particular order of objects. It should be noted that terms used in this manner may be interchangeable in some cases, allowing the embodiments of the present application to be implemented in an order other than that shown or described herein. The objects distinguished by "first" and "second" are generally similar, and the number of objects is not limited; for example, there may be one or more first communication devices. In the specification and claims, "and / or" indicates at least one of the connected objects, and the symbol " / " generally indicates that the related objects before and after are in an "or" relationship.

[0018] It should be noted that the techniques described in the embodiments of the present application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, and may also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-Carrier Frequency-Division Multiple Access (SC-FDMA), and other systems. In the embodiments of the present application, the terms "system" and "network" are often used interchangeably, and the techniques described herein may be used in the above systems and wireless communication technologies, or in other systems and wireless communication technologies. However, for illustrative purposes, the following description will describe a New Radio (NR) system, and NR terminology will be used in most of the following description, but these technologies are applicable to systems other than NR systems, such as 6th Generation (6G) communication systems.

[0019] The communication devices described in the embodiments of the present application (e.g., the first communication device, the second communication device, the third communication device, the fourth communication device, and the fifth communication device, etc.) may be core network devices and may be referred to as network elements or network nodes.Core network devices include core network nodes, core network functions, application functions (AF), network data analytics functions (NWDAF), unified data management (UDM), network exposure functions (NEF), local NEFs (L-NEFs), operation administration and maintenance (OAM), user plane functions (UPF), session management functions (SMF), data collection application functions (DC-AF), mobility management entities (MMEs), access and mobility management functions (AMFs), user plane functions (UPF), policy control functions (PCFs), policy and charging rules functions (PCRFs), edge application server discovery functions (EASDFs), and unified data repositories (UDMs). The network configuration may include, but is not limited to, at least one of a Network Repository (UDR), a Home Subscriber Server (HSS), a Centralized Network Configuration (CNC), a Network Repository Function (NRF), a Binding Support Function (BSF), and the like.In the embodiments of the present application, a core network device in an NR system is used as an example, but the specific type of core network device is not limited.

[0020] In the embodiments of the present application, the candidate devices may include terminals (which may be referred to as terminal devices or user equipment (UE)) or any other feasible devices. The terminals may be terminal-side devices such as mobile phones, tablet personal computers, laptop computers (also called notebook computers), personal digital assistants (PDAs), personal digital assistants, netbooks, ultra-mobile personal computers (UMPCs), mobile internet devices (MIDs), augmented reality (AR) / virtual reality (VR) devices, robots, wearable devices, vehicle-mounted equipment (VUEs), pedestrian-mounted equipment (PUEs), smart home devices (household devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, furniture, etc.), game consoles, personal computers (PCs), automated teller machines (ATMs), or kiosks. Wearable devices include smart watches, smart bracelets, smart earphones, smart glasses, smart accessories (smart bangles, smart hand chains, smart rings, smart necklaces, smart anklets, smart wristbands, smart clothing, etc.) etc. Note that the specific type of terminal is not limited in the embodiments of this application.

[0021] Next, the device determination method provided by the embodiments of the present application will be described in detail based on the embodiments and their application scenarios with reference to the drawings.

[0022] As shown in FIG. 1, an embodiment of the present application provides a device determination method that may include steps 201 to 204 described below.

[0023] In step 201, the first communication device sends a first request message to the second communication device.

[0024] In step 202, the second communication device receives a first request message from the first communication device.

[0025] The first request message may be for requesting to obtain network performance analysis information.

[0026] In step 203, the second communication device sends network performance analysis information to the first communication device.

[0027] In step 204, the first communication device receives network performance analysis information from the second communication device.

[0028] The network performance analysis information may include network performance analysis information corresponding to M candidate devices, where M is a positive integer. The network performance analysis information corresponding to one candidate device may be: a) radio access method information of the one candidate device; b) information about the time when the candidate device can participate in federated learning; c) location information of the one candidate device; d) information about the time period covered by the network of the one candidate device; and e) information on the ratio of the time period covered by the network of the one candidate device during the time period of interest to the time period of interest; f) network signal quality information of the one candidate device.

[0029] In an embodiment of the present application, after receiving the first request message, the second communication device may send network performance analysis information corresponding to the M candidate devices to the first communication device, so that the first communication device obtains the network performance of each of the M candidate devices and determines the candidate devices that meet the network performance requirements of the federated learning as devices to participate in the federated learning, that is, the first communication device can select suitable candidate devices for federated learning.

[0030] In an embodiment of the present application, since the network performance analysis information can indicate the network performance corresponding to M candidate devices, when the first communication device receives the network performance analysis information, it can obtain the network performance corresponding to each of the M candidate devices, and determine the candidate devices that meet the network performance requirements of the federated learning as the devices to participate in the federated learning.

[0031] In the embodiments of the present application, the first communication device may include an AF or any other feasible consumer service entity, the second communication device may include an NWDAF, and the candidate device may include a UE or any other feasible device. Specifically, it can be determined according to actual usage needs, and the embodiments of the present application are not limited thereto.

[0032] In the embodiment of the present application, the radio access scheme may be a non-third generation partnership project (non-3GPP®) radio access scheme, such as a wireless local area network (WLAN), and may be a 3GPP® radio access scheme. methodFor example, it may be a fourth generation (4G) evolved universal terrestrial radio access network (EUTRAN or E-UTRAN) or a fifth generation (5G) NR.

[0033] For example, with regard to the above a), the radio access method information of one candidate device may indicate that the radio access method of the candidate device is non-3GPP (registered trademark) WLAN.

[0034] In an embodiment of the present application, with regard to the above b), the time information of one candidate device during which it can participate in federated learning may indicate the time during which the candidate device can participate in federated learning, for example, 00:00-04:00 every day.

[0035] In the embodiment of the present application, with regard to c) above, the location information of one candidate device may indicate the region, cell or tracking area (TA) in which the candidate device is located.

[0036] In an embodiment of the present application, the time of interest may be a time that the first communication device is interested in, for example, a time when the first communication device is scheduled to perform federated learning. For example, the time when the first communication device is scheduled to perform federated learning is 01:00-03:00 on March 15, 2020.

[0037] Regarding d) above, the time period information covered by the network of one candidate device may indicate the length of time or the time period covered by the network of the candidate device.

[0038] Regarding e) above, information on the proportion of the time period covered by the network of one candidate device within the time of interest relative to the time of interest (hereinafter simply referred to as proportion information of the time covered by the network) may indicate the ratio between the length of time covered by the network of the candidate device within the time of interest and the length of time corresponding to the time of interest.

[0039] For example, if the candidate device is a terminal and the wireless access method is a wireless local area network (WLAN), the time of interest is three hours from 8:00 to 11:00, and the terminal is covered by wireless fidelity (Wi-Fi) for two of these three hours, then the proportion of the time period covered by Wi-Fi for the terminal to the time of interest is 2 / 3.

[0040] Alternatively, in the embodiment of the present application, the network coverage time ratio information may indicate the ratio of the time period covered by the network of the candidate device to the time of interest by a level, such as, but not limited to, "high," "medium," or "low." Alternatively, the network coverage time ratio information may further indicate the ratio of the time period covered by the network of the candidate device to the time of interest in the form of a decimal, a fraction, a percentage, etc. Specifically, it may be determined according to actual usage needs, and the embodiment of the present application is not limited thereto.

[0041] In an embodiment of the present application, the network signal quality information is for indicating a network signal quality of the device, which may include at least one of signal quality, signal strength, and signal stability.

[0042] The network signal quality may be indicated by an average or peak value of a network signal quality parameter.

[0043] In an embodiment of the present application, if the wireless access method is WLAN, the network signal quality may be indicated by at least one parameter of a received signal strength indication (RSSI) and a round trip time (RTT).

[0044] Optionally, in an embodiment of the present application, the first request message may include reporting granularity indication information. The reporting granularity indication information may be for instructing the candidate device to report network performance analysis information corresponding to the candidate device with device granularity. For example, the reporting granularity indication information may instruct the candidate device to report network performance analysis information corresponding to the candidate UE with UE (per UE) granularity. In this way, the first communication device can determine the network performance of each UE among the M candidate UEs to select an appropriate UE to participate in federated learning.

[0045] Optionally, in the embodiment of the present application, the first request information may include filtering information, which may include at least one of the following:

[0046] a) Area of ​​interest, which may also be called an area of ​​interest, for example one or more cells or one or more tracking areas (TA).

[0047] b) Radio access method limitation information, which may be used to indicate the radio access method of the candidate device, and the description of the radio access method may refer to the relevant description in the above embodiment.

[0048] c) Interest time: The interest time may be a time when the first communication device is scheduled to perform federated learning, such as 00:00-04:00 on March 15, 2020, and may be determined based on actual usage needs.

[0049] Optionally, in an embodiment of the present application, the filtering information may further include at least one of the following:

[0050] d) The number of candidate devices that need to be replied by the second communication device, i.e., the value of M, for example, M=500.

[0051] e) network signal quality limitation information of the candidate device, which may be for indicating a network signal quality threshold required for the candidate device, which may be a minimum requirement for the network signal quality of the candidate device;

[0052] As an example, using signal stability (which may be expressed as the percentage of time that network signal strength remains above a target value), a network signal stability threshold of 90% indicates that the signal strength of the candidate device is required to reach the target value for 90% or more of the time during the time of interest.

[0053] f) Algorithm Qualification Information: The algorithm qualification information may be for indicating algorithms related to artificial intelligence (AI) data analysis tasks such as machine learning that the required candidate device supports, e.g., deep learning, linear regression, etc.

[0054] g) Model training accuracy limit information. The model training accuracy limit information may indicate the training accuracy that the available model can achieve when the requested candidate device participates in federated learning, i.e., the model accuracy that the available model can achieve after training is complete. That is, the ratio of the number of correct predictions (judgments) made by the model after training is complete to the total number of predictions, for example, an accuracy rate of 90%.

[0055] h) Model training speed limit information. The model training speed limit information may indicate the training time required to train a model available when a requested candidate device participates in federated learning to a first training accuracy (e.g., an 80% accuracy rate). Specifically, the training speed limit information may indicate the training time required for the model to achieve the first training accuracy when the candidate device locally trains the model. The longer the required training time, the slower the training speed, and the shorter the required training time, the faster the training speed.

[0056] i) Federated learning memory space limit information: The memory space limit information indicates the amount of memory space that the requested candidate device reserves for information such as models and data for federated learning, for example, 10 megabits (MB).

[0057] Optionally, in an embodiment of the present application, the first request message may further include a network performance analytic identifier (analytic ID), which may indicate a task corresponding to the first request message, such as, for example, obtaining network performance information of candidate devices that meet a filtering information requirement. Exemplarily, the network performance analytic identifier may be WLAN performance or NR performance.

[0058] Optionally, in the embodiment of the present application, the first request message may further include reporting limitation information. The reporting limitation information may include at least one of the following:

[0059] A) Sorting information of candidate devices. The sorting information is for instructing the second communication device to output the candidate devices in ascending or descending order of a certain parameter / scale. If the signal strength is output in descending order, when the second communication device returns the result (i.e., transmits the network performance analysis information to the first communication device), the candidate devices may be arranged in ascending order of signal strength.

[0060] B) Grouping information of candidate devices. The grouping information is for instructing the second communication device to group candidate devices according to certain parameters / factors (e.g., time, location, etc.). For example, the second communication device may group candidate devices that perform federated learning between 10:00 and 12:00 noon into one group among all candidate devices.

[0061] C) The percentage of the time period covered by the candidate device's network during the time of interest relative to the time of interest (i.e., the percentage of time covered by the network). For the explanation of the proportion of time covered by the network, please refer to the relevant explanation in the above embodiment, and to avoid redundancy, it will not be repeated here.

[0062] D) Network performance analysis information format.

[0063] E) Contents included in network performance analysis information.

[0064] In the device determination method provided in the embodiments of the present application, the device's radio access method information, time information available for participation in federated learning, location information, time period information covered by the network, ratio information of the time period covered by the network to the time of interest, and network signal quality information can all reflect the network performance corresponding to the device. Therefore, when the first communication device receives the network performance analysis information, it can determine the network performance corresponding to each of the M candidate devices, so that the first communication device can determine the candidate devices that meet the network performance requirements of federated learning as devices to participate in federated learning, that is, the first communication device can select appropriate devices to participate in federated learning.

[0065] Optionally, before the above step 203, the device determination method provided in the embodiment of the present application may further include step 205 and step 206 described below.

[0066] In step 205, the first communication device determines N devices to participate in federated learning from the M candidate devices based on the network performance analysis information, where N is a positive integer less than or equal to M.

[0067] In step 206, the first communication device establishes connections with the N devices and performs federated learning.

[0068] In an embodiment of the present application, after receiving the network performance analysis information, the first communication device may determine N devices from the M candidate devices to participate in federated learning, and then establish connections with the N devices to perform federated learning, thereby obtaining a federated learning model that meets the needs of the first communication device.

[0069] For example, if the first communication device limits the UEs that need to connect to a WLAN and whose signal strength must reach a threshold in the filtering information and limit federated learning to be performed within City A on March 15, 2020, the M candidate UEs are UEs that meet these conditions. The first communication device may select the M candidate UEs based on the number of UEs that overlap on March 15, 2020. For example, if there are 500 UEs that meet the conditions and can participate in federated learning between 2:00 PM and 3:00 PM on March 15, 2020, and 2:00 PM to 3:00 PM is the time with the largest number of UEs that overlap on that day, the first communication device may select these 500 UEs as devices that will participate in federated learning. As a result, the first communication device can establish connections with these 500 UEs between 2:00 PM and 3:00 PM on March 15, 2020, and perform federated learning. Alternatively, the first communication device may select the UEs with the best network signal strength as devices that will participate in federated learning.

[0070] Optionally, in an embodiment of the present application, before the above step 205, the device determination method provided in the embodiment of the present application may further include at least one of step 207 and step 208 described below.

[0071] It should be noted that the present application does not limit the specific order / timing of execution of step 207 and step 208, and step 207 and step 208 may be executed before step 201 or after step 204. Specifically, this may be determined according to the needs of actual use, and the embodiments of the present application are not limited thereto.

[0072] For step 207, the first communication device determines that the M candidate devices are willing to engage in federated learning.

[0073] For step 208, the first communication device determines that the M candidate devices are capable of federated learning.

[0074] In an embodiment of the present application, before determining N devices from M candidate devices to participate in federated learning, the first communication device may determine in advance whether the M candidate devices are willing and / or capable of federated learning based on network performance analysis information, and if it determines that the M candidate devices are willing and / or capable of federated learning, it can determine N devices from the M candidate devices to participate in federated learning.

[0075] Alternatively, in the embodiment of the present application, the above step 207 may be realized by the following steps 207a and 207b.

[0076] In step 207a, the first communication device obtains federated learning willingness information of the M candidate devices from the third communication device.

[0077] In step 207b, the first communication device determines that the M candidate devices are willing to engage in federated learning based on the federated learning willingness information of the M candidate devices.

[0078] In an embodiment of the present application, after the first communication device obtains the federated learning willingness information of the M candidate devices from the third communication device, it can determine that the M candidate devices are willing to engage in federated learning based on the federated learning willingness information of the M candidate devices.

[0079] Optionally, in an embodiment of the present application, the first communication device obtains federated learning willingness information of Q devices from a third communication device, and selects federated learning willingness information of the M candidate devices from the federated learning willingness information of the Q devices, where the Q devices may include the M candidate devices.

[0080] In an embodiment of the present application, the third communication device may be a UDM.

[0081] Optionally, in an embodiment of the present application, the motivation information of the associative learning is: Indication of willingness to participate in associative learning; and condition information for participating in federated learning.

[0082] Optionally, in an embodiment of the present application, the condition information for participating in the federated learning may include at least one of the following:

[0083] The wireless access method used when participating in federated learning, such as non-3GPP (registered trademark) WLAN.

[0084] Time to participate in federated learning. That is, the time when you can participate in federated learning, for example, 2:00 AM - 5:00 AM.

[0085] The location when participating in federated learning. For example, the area where the participant is located or the cell they accessed when participating in federated learning.

[0086] Alternatively, in the embodiment of the present application, the above step 208 may be realized by the following steps 208a and 208b.

[0087] In step 208a, the first communication device obtains federated learning capability information of the M candidate devices from the third communication device.

[0088] In step 208b, the first communication device determines that the M candidate devices are capable of federated learning based on the federated learning capability information of the M candidate devices.

[0089] In an embodiment of the present application, after the first communication device obtains the federated learning capability information of the M candidate devices from the third communication device, it can determine that the M candidate devices are capable of federated learning based on the federated learning capability information of the M candidate devices.

[0090] Optionally, in an embodiment of the present application, the first communication device may receive federated learning information from a third communication device of S devices. ability The information is acquired and federated learning of the S devices is performed. ability From the information, the federated learning of the above M candidate devices ability The S devices may include M candidate devices.

[0091] Optionally, in an embodiment of the present application, the above-mentioned federated learning capability information may include at least one of the following:

[0092] Models that can be used to participate in federated learning, such as models using specific network architectures like Residual Networks (ResNet), Inception-v3, etc.

[0093] Algorithms that can be used when participating in federated learning, such as gradient descent.

[0094] The training accuracy of a model that can be achieved when participating in federated learning. This refers to the model accuracy that the available model can achieve after training is complete, for example, the highest achievable accuracy. This refers to the percentage of the total number of accurate predictions or judgments that the model can make after training is complete, for example, a 90% accuracy rate.

[0095] The training speed of the model that can be achieved when participating in federated learning. The training speed is the training time required to train the model that can be used when participating in federated learning to the first training accuracy, i.e., the first communication When the device locally trains the available model, this indicates the training time (e.g., 30 minutes) required to train the model to a first training accuracy (e.g., 80%); the longer the training time, the slower the training speed; the shorter the training time, the faster the training speed.

[0096] The size of the memory space participating in federated learning. That is, the size of the memory space reserved for information such as federated learning models and data, for example, 15 MB.

[0097] Optionally, before the above step 207a or step 208a, the device determination method provided in the embodiment of the present application may further include step 209 described below.

[0098] In step 209, the first communication device acquires the network function disclosure information of the third communication device from the fifth communication device.

[0099] The fifth communication device may be an NEF or other communication device, and can be specifically determined according to the actual usage needs.

[0100] The first communication device may exchange information with the third communication device by sending second request information to the fifth communication device to request acquisition of public information of the network capabilities of the third communication device. For example, the first communication device may acquire federated learning willingness information and / or federated learning capability information of the M candidate devices from the third communication device.

[0101] Optionally, in an embodiment of the present application, before the above step 203, the device determination method provided in the embodiment of the present application may further include step 210 and step 211 described below.

[0102] In step 210, the second communication device obtains network performance data corresponding to the M candidate devices from the fourth communication device.

[0103] In step 211, the second communication device analyzes network performance data corresponding to the M candidate devices to obtain network performance analysis information corresponding to the M candidate devices.

[0104] In an embodiment of the present application, the second communication device obtains network performance data corresponding to the M candidate devices from the fourth communication device, and then analyzes the network performance data corresponding to the M candidate devices to obtain network performance analysis information corresponding to the M candidate devices.

[0105] Optionally, in the embodiment of the present application, the fourth communication device may include at least one of network elements such as SMF, OAM, UDM and DC-AF.

[0106] Optionally, in the embodiment of the present application, the above step 210 may be realized by at least one of step 210a and step 210b described below.

[0107] In step 210a, the second communication device obtains at least one of radio access method information, time information covered by the network, and time information in the network of the session corresponding to the M candidate devices from the SMF.

[0108] In step 210b, the second communication device obtains at least one of network identifier information, network signal quality information corresponding to the M candidate devices from a network management device.

[0109] For example, taking the second communication device as an NWDAF, the NWDAF may acquire network signal quality information corresponding to the M candidate devices, such as signal quality information (e.g., RTT, RSSI, etc.) for devices connecting to a WLAN, from the OAM, and may also acquire network identifier information corresponding to the M candidate devices, such as a Service Set Identifier (SSID), from the OAM. The NWDAF may also acquire radio access method information corresponding to the M candidate devices, such as WLAN, 5G NR, or 4G EUTRAN, from the SMF, and may also acquire network coverage time information corresponding to the M candidate devices, such as the time covered by a WLAN, from the SMF. The NWDAF may also acquire algorithm information supported by the M candidate devices, achievable model training accuracy information, etc. from the UDM or DCAF. The NWDAF may also acquire flow rate information corresponding to the M candidate devices from the UPF.

[0110] In an embodiment of the present application, after obtaining network performance data corresponding to the M candidate devices, the second communication device can analyze the network performance data of the M candidate devices to obtain network performance analysis results for the M candidate devices, and further generate network performance analysis information corresponding to the M candidate devices based on the network performance analysis results for the M candidate devices.

[0111] Hereinafter, with reference to Table 1, the network performance analysis results of the device will be described by taking the case where the wireless access method of the device is WLAN as an example.

[0112] [Table 1]

[0113] Optionally, in an embodiment of the present application, before the above step 203, the device determination method provided in the embodiment of the present application may further include step 212 described below.

[0114] In step 212, the second communication device determines M candidate devices based on the filtering information included in the first request message.

[0115] Each candidate device among the M candidate devices: being located within a region of interest; The radio access method is a radio access method indicated by the radio access method limitation information; and be covered by the network within the time period of interest.

[0116] Optionally, in an embodiment of the present application, each candidate device among the M candidate devices: the network signal quality is equal to or greater than the network signal quality indicated by the network signal quality limit information; The supported algorithms are those indicated by the algorithm limitation information; and The training accuracy of the model that can be achieved when participating in federated learning is equal to or greater than the training accuracy indicated by the model's training accuracy limit information; The training speed of the model that can be achieved when participating in associative learning is equal to or greater than the training speed indicated by the model's training speed limit information; At least one condition may further be satisfied: the size of the memory space participating in the associative learning is equal to or greater than the size of the memory space indicated by the memory space limitation information for the associative learning.

[0117] For example, if the training accuracy indicated by the training accuracy limitation information of the model is 85%, and if the training accuracy of a model that a device among the K devices can achieve during federated learning is 93%, the device may be determined to be a candidate device.

[0118] Optionally, in an embodiment of the present application, before the above step 203, the device determination method provided in the embodiment of the present application may further include step 213 and / or step 214 described below.

[0119] In step 213, the second communication device determines that the M candidate devices are willing to engage in federated learning.

[0120] In step 214, the second communication device determines that the M candidate devices are capable of federated learning.

[0121] In an embodiment of the present application, before the second communication device transmits the network performance analysis information to the first communication device, 2 The communication device may determine in advance whether the M candidate devices are willing and / or capable of federated learning. If it is determined that the M candidate devices are willing and / or capable of federated learning, the second communication device may send network performance analysis information corresponding to the M candidate devices to the first communication device.

[0122] Alternatively, in the embodiment of the present application, the above step 213 may be realized by the following steps 213a and 213b.

[0123] In step 213a, the second communication device obtains federated learning willingness information of the M candidate devices from the third communication device.

[0124] In step 213b, the second communication device determines that the M candidate devices are willing to engage in federated learning based on the federated learning willingness information of the M candidate devices.

[0125] In an embodiment of the present application, when the second communication device obtains the federated learning willingness information of the M candidate devices from the third communication device, 2 The communication device may determine that the M candidate devices are willing to participate in federated learning based on the willingness information of the M candidate devices for federated learning, and may thereby send network performance analysis information corresponding to the M candidate devices willing to participate in federated learning to the first communication device. In this way, the first communication device can directly select devices to participate in federated learning from the M candidate devices.

[0126] Optionally, in an embodiment of the present application, the second communication device obtains federated learning willingness information of W devices from a third communication device, and selects federated learning willingness information of the M candidate devices from the federated learning willingness information of the W devices, where the W devices may include the M candidate devices.

[0127] In an embodiment of the present application, the third communication device may be a UDM.

[0128] For the explanation of the motivation information for associative learning, please refer to the relevant explanation in the above embodiment, and to avoid duplication, it will not be repeated here.

[0129] Alternatively, in the embodiment of the present application, the above step 214 may be realized by the following steps 214a and 214b.

[0130] In step 214a, the second communication device obtains federated learning capability information of the M candidate devices from the third communication device.

[0131] In step 214b, the second communication device determines that the M candidate devices are capable of federated learning based on the federated learning capability information of the M candidate devices.

[0132] In an embodiment of the present application, when the second communication device obtains the federated learning capability information of the M candidate devices from the third communication device, 2 The communication device may determine that the M candidate devices are capable of federated learning based on the federated learning capability information of the M candidate devices, and may thereby send network performance analysis information corresponding to the M candidate devices capable of federated learning to the first communication device. In this way, the first communication device can directly select devices to participate in federated learning from the M candidate devices.

[0133] Optionally, in an embodiment of the present application, the second communication device obtains federated learning capability information of the P devices from the third communication device, and determines the federated learning capability of the P devices. Ability information The federated learning capability information of the M candidate devices is selected from the P devices. Note that the P devices may include the M candidate devices.

[0134] For the explanation of the ability information of the associative learning, please refer to the relevant explanation in the above embodiment, and to avoid duplication, it will not be repeated here.

[0135] Optionally, before the above step 213a or step 214a, the device determination method provided in the embodiment of the present application may further include step 215 described below.

[0136] In step 215, the second communication device obtains the network capability disclosure information of the third communication device from the fifth communication device.

[0137] The fifth communication device may be an NEF or other communication device, and can be specifically determined according to the actual usage needs.

[0138] The second communication device may exchange information with the third communication device by sending second request information to the fifth communication device to request acquisition of public information of the network capabilities of the third communication device. For example, the second communication device may acquire federated learning willingness information and / or federated learning capability information of the M candidate devices from the third communication device.

[0139] Hereinafter, and with reference to FIGS. 2 and 3, a device determination method provided in an embodiment of the present application will be exemplarily described.

[0140] As shown in FIG. 2, in step 0a, a consumer service entity such as an AF obtains network capability disclosure information about a communication device such as a UDM / NRF / DCAF by sending a request to an NEF to later obtain federated learning willingness information and / or federated learning capability information of a candidate device from the communication device such as a UDM, and determine whether the device has federated learning capability and / or federated learning willingness.

[0141] In step 0b, a consumer service entity such as an AF requests a capability memory network element such as a UDM / NRF / DC-AF to acquire federated learning willingness information and / or federated learning capability information.

[0142] As shown in FIG. 3, in step 1, a consumer service entity such as an AF sends a first request message (Nnwdaf_AnalyticsInfo or Nnwdaf_AnalyticsSubscription can be used) to an NWDAF to request acquisition of network performance analysis information.

[0143] In step 2, the NWDAF obtains network performance data, such as radio access method and signal quality, related to the UE from a data provider, such as an SMF, an OAM, or a UDM, based on the task description and constraints of the first request message. Step 2 may include steps 2a, 2b, and 2c.

[0144] In step 3, the NWDAF uses and analyzes the acquired network performance data to obtain UE-granular network performance analysis results, thereby obtaining network performance analysis information.

[0145] In step 4, the NWDAF returns a task response message based on the description information of the first request message in step 1, i.e., sends network performance analysis information to a consumer service entity such as an AF. The NWDAF may respond based on the Nnwdaf_AnalyticsInfo or Nnwdaf_AnalyticsSubscription used in step 1.

[0146] In step 5, the consumer service entity, such as the AF, determines the UE(s) to participate in the federated learning based on the response message returned in step 4.

[0147] In step 6, the consumer service entity such as the AF establishes a connection with the UE(s) based on the identifier information of the UE(s) participating in the federated learning determined in step 5, and performs federated learning.

[0148] Furthermore, the device determination method provided in the embodiments of the present application may have an execution body as a device determination apparatus. In the embodiments of the present application, the device determination apparatus provided in the embodiments of the present application will be described taking the case where the device determination apparatus executes the device determination method as an example.

[0149] 4 , an embodiment of the present application provides a device determination apparatus 300, which includes a sending module 301 and a receiving module 302. The sending module 301 is for sending a first request message to a second communication device to request acquisition of network performance analysis information, and the receiving module 302 is for receiving the network performance analysis information from the second communication device, where the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer. The network performance analysis information corresponding to one candidate device includes at least one of the following information: radio access method information of the candidate device; time information of the candidate device available for participating in federated learning; location information of the candidate device; time period information covered by the network of the candidate device; information on the ratio of the time period covered by the network of the candidate device within the time period of interest to the time period of interest; and network signal quality information of the candidate device.

[0150] Optionally, the device determination apparatus further includes a determination module and an execution module, where the determination module is for determining N devices to participate in federated learning from M candidate devices based on network performance analysis information, where N is a positive integer equal to or less than M, and the execution module is for establishing connections with the N devices and performing federated learning.

[0151] Optionally, the first request message includes reporting granularity instruction information for instructing the device to report network performance corresponding to the candidate device as a granularity.

[0152] Optionally, the first request message comprises: a region of interest; Radio access method limited information; and a time of interest.

[0153] Optionally, the determination module is further for determining that the M candidate devices are willing to engage in federated learning, and / or the determination module is further for determining that the M candidate devices are capable of federated learning.

[0154] Optionally, the determination module includes an acquisition submodule for acquiring federated learning willingness information of the M candidate devices from the third communication device, and a determination submodule for determining that the M candidate devices are willing to engage in federated learning based on the federated learning willingness information of the M candidate devices.

[0155] Optionally, the determination module includes an acquisition submodule for acquiring federated learning capability information of the M candidate devices from the third communication device, and a determination submodule for determining that the M candidate devices are capable of federated learning based on the federated learning capability information of the M candidate devices.

[0156] Optionally, motivation information for associative learning is Indication of willingness to participate in associative learning; and condition information for participating in federated learning.

[0157] Optionally, the condition information for participating in the federated learning is Wireless access method when participating in federated learning, time to participate in federated learning, and at least one of the positions when participating in federated learning.

[0158] Optionally, the ability information of associative learning is Models available for participating in federated learning and Algorithms available for participating in federated learning and The training accuracy of models achievable when participating in federated learning, a training rate for indicating the training time required to train the available models to a first training accuracy achievable when participating in federated learning; and the size of the memory space participating in the associative learning.

[0159] Optionally, the second communication device includes an NWDAF.

[0160] An embodiment of the present application provides a device determination device, wherein the wireless access method information of a device, the time information available for participating in federated learning, location information, time period information covered by the network, the ratio information of the time period covered by the network to the time of interest, and network signal quality information can all reflect the network performance corresponding to a device. Therefore, upon receiving the network performance analysis information, the device determination device can determine the network performance corresponding to each of the M candidate devices, and thereby determine the candidate devices that meet the network performance requirements of federated learning as devices to participate in federated learning, that is, the device determination device can select appropriate devices to participate in federated learning.

[0161] 5 , an embodiment of the present application provides a device determination apparatus 400, which includes a receiving unit 401 and a sending unit 402. The receiving unit 401 may be configured to receive a first request message from a first communication device to request acquisition of network performance analysis information. The sending unit is configured to send the network performance analysis information to the first communication device, where the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer. The network performance analysis information corresponding to a candidate device includes at least one of the following information: radio access method information of the candidate device; time information of the candidate device available to participate in federated learning; location information of the candidate device; time period information covered by the network of the candidate device; ratio information of the time period covered by the network of the candidate device within a time period of interest to the time period of interest; and network signal quality information of the candidate device.

[0162] Optionally, the first request message includes reporting granularity instruction information for instructing the device to report network performance corresponding to the candidate device as a granularity.

[0163] Optionally, the first request message comprises: a region of interest; Radio access method limited information; and a time of interest.

[0164] Optionally, the device determination apparatus further includes: an acquisition unit for acquiring network performance data corresponding to the M candidate devices from the fourth communication device; and an analysis unit for analyzing the network performance data corresponding to the M candidate devices and obtaining network performance analysis information corresponding to the M candidate devices.

[0165] Optionally, the acquiring unit includes: a first acquiring subunit for acquiring at least one of radio access method information corresponding to the M candidate devices, time information covered by the network, and time information in the network of the session from a session management function network element SMF; and a second acquiring subunit for acquiring at least one of network identifier information corresponding to the M candidate devices and network signal quality information from a network management device.

[0166] Optionally, the device determination apparatus further includes a determination unit for determining the M candidate devices based on filtering information included in the first request message, and each candidate device among the M candidate devices: being located within a region of interest; The radio access method is a radio access method that is a radio access method indicated by the radio access method limitation information; and be covered by the network within the time period of interest.

[0167] Optionally, the device determination apparatus further includes a determining unit for determining that the M candidate devices are willing to perform federated learning, and / or a determining unit for determining that the M candidate devices are capable of performing federated learning.

[0168] Optionally, the determination unit includes an acquisition subunit for acquiring federated learning willingness information of the M candidate devices from the third communication device, and a determination subunit for determining that the M candidate devices are willing to engage in federated learning based on the federated learning willingness information of the M candidate devices.

[0169] Optionally, the determination unit includes an acquisition subunit for acquiring federated learning capability information of the M candidate devices from the third communication device, and a determination subunit for determining that the M candidate devices are capable of federated learning based on the federated learning capability information of the M candidate devices.

[0170] Optionally, motivation information for associative learning is Indication of willingness to participate in associative learning; and condition information for participating in federated learning.

[0171] Optionally, the condition information for participating in the federated learning is Wireless access method when participating in federated learning, time to participate in federated learning, and at least one of the positions when participating in federated learning.

[0172] Optionally, the ability information of associative learning is Models available for participating in federated learning and Algorithms available for participating in federated learning and The training accuracy of models achievable when participating in federated learning, a training rate to indicate the training time required to train the available models to a first training accuracy achievable when participating in federated learning; and the size of the memory space participating in the associative learning.

[0173] An embodiment of the present application provides a device determination device, wherein the wireless access method information of a device, the time information available for participating in federated learning, location information, time period information covered by the network, the ratio information of the time period covered by the network to the time of interest, and network signal quality information can all reflect the network performance corresponding to the device. After the device determination device sends network performance analysis information to the first communication device, the first communication device can determine the network performance corresponding to each candidate device among the M candidate devices, so that the first communication device can determine the candidate devices that meet the network performance requirements of federated learning as devices to participate in federated learning, i.e., can select appropriate devices to participate in federated learning.

[0174] The device determination apparatus in the embodiments of the present application may be an electronic device, for example, an electronic device having an operating system, or a component of an electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal or other device other than a terminal. Exemplarily, the terminal may include, but is not limited to, the types of terminal 11 listed above. The other device may be a server, a network attached storage (NAS), etc., and the embodiments of the present application do not specifically limit the scope of the present application.

[0175] The device determination apparatus provided in the embodiments of the present application can implement each procedure implemented by the above method embodiments, and achieves similar technical effects, so they will not be repeated here to avoid redundancy.

[0176] Optionally, as shown in FIG. 6 , an embodiment of the present application further provides a communication device 500 including a processor 501 and a memory 502, and storing a program or command executable by the processor 501. For example, if the communication device 500 is a first communication device, when the program or command is executed by the processor 501, it can realize each step of the embodiment of the device determination method and achieve similar technical effects; if the communication device 500 is a second communication device, when the program or command is executed by the processor 501, it can realize each step of the embodiment of the device determination method and achieve similar technical effects, which will not be repeated here to avoid redundancy.

[0177] An embodiment of the present application further provides a communication device, including a processor and a communication interface, where, when the communication device is a first communication device, the communication interface is for sending a first request message to a second communication device and receiving network performance analysis information from the second communication device, and when the communication device is the second communication device, the communication interface is for receiving the first request message from the first communication device and sending network performance analysis information to the first communication device, the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer, and the network performance analysis information corresponding to one candidate device includes at least one of: radio access method information of the one candidate device, time information of the one candidate device available to participate in federated learning, location information of the one candidate device, time period information covered by the network of the one candidate device, information on a ratio of the time period covered by the network of the one candidate device within a time period of interest to the time period of interest, and network signal quality information of the one candidate device. The embodiments of the communication device correspond to the method embodiments of the first or second communication device, and the implementation steps and means of the method embodiments are all applicable to the embodiments of the communication device, and can achieve similar technical effects.

[0178] Specifically, an embodiment of the present application further provides a communication device 600. As shown in Fig. 7, the communication device 600 includes a processor 601, a network interface 602, and a memory 603, where the network interface 602 is, for example, a common public radio interface (CPRI).

[0179] The communication device 600 of the embodiment of the present invention further includes commands or programs stored in the memory 603 and executable by the processor y01. The processor 601 can call the commands or programs in the memory y03 to execute the methods executed by each module in the device determination apparatus, thereby achieving similar technical effects, and therefore will not be repeated here to avoid redundancy.

[0180] The embodiments of the present application further provide a readable storage medium, which stores a program or command, and when the program or command is executed by a processor, can realize each step of the above-mentioned device determination method embodiment and achieve the same technical effect, so that the description will not be repeated here to avoid redundancy.

[0181] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, etc.

[0182] The embodiments of the present application further provide a chip, which includes a processor and a communication interface coupled thereto, and the processor executes programs or commands to realize the steps of the embodiments of the device determination method described above, and can achieve similar technical effects, so that the description will not be repeated here to avoid redundancy.

[0183] The chip described in the embodiments of the present application is also called a system on a chip, a system chip, a chip system, or an SoC.

[0184] The embodiments of the present application further provide a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the above-described device determination method embodiments and achieve similar technical effects, and therefore will not be described again here to avoid redundancy.

[0185] It should be noted that, as used herein, the terms "comprise," "consist," and any other variations thereof are intended to include a non-exclusive inclusion, such that a process, method, article, or apparatus comprising a set of elements includes not only those elements but also other elements not expressly specified or inherent in such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprises a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element. Furthermore, the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed herein, and may further include performing functions substantially simultaneously or in the reverse order, depending on the functionality involved. For example, the methods described above may be performed in a different order than described, and further, steps may be added, omitted, or combined. Furthermore, features described with reference to some examples may be combined with other examples.

[0186] From the above description of the embodiments, it will be clear to those skilled in the art that the methods of the above embodiments can be realized in the form of a combination of software and a necessary common hardware platform. Of course, hardware implementation is also possible, but in many cases the former is a more preferred embodiment. Based on this view, the technical means of the present application, or a portion that contributes to the prior art, can be embodied as a software product, and the computer software product is stored in a storage medium (e.g., ROM / RAM, magnetic disk, optical disk) and includes a plurality of commands that cause a terminal (which may be a mobile phone, computer, server, air conditioner, network device, etc.) to execute the method of each embodiment of the present application.

[0187] Although the examples of the present application have been described above with reference to the drawings, the present application is not limited to the above-mentioned specific embodiments, which are merely illustrative and not limiting. Based on the suggestions of the present application, many forms that a person skilled in the art can obtain without departing from the spirit of the present application and the scope of protection of the claims are all within the scope of protection of the present application.

[0188] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims priority to a Chinese patent application filed with the Intellectual Property Office of the People's Republic of China on March 28, 2022, bearing application number 202210314847.1 and entitled "Device determination method, apparatus and communication device," the entire contents of which are incorporated herein by reference.

Claims

1. a first communication device sending a first request message to a second communication device to request obtaining network performance analysis information; receiving, by the first communication device, the network performance analysis information from the second communication device, wherein the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer; The network performance analysis information corresponding to one candidate device is Time information of the one candidate device available to participate in federated learning; time period information covered by the network of the one candidate device; and network signal quality information of the one candidate device.

2. After the step of the first communication device receiving the network performance analysis information from the second communication device, the device determination method includes: The first communication device determines N devices to participate in the federated learning from the M candidate devices based on the network performance analysis information, where N is a positive integer equal to or less than M; The device determination method according to claim 1 , further comprising the step of: the first communication device establishing a connection with the N devices and performing federated learning.

3. The device determination method according to claim 1 , wherein the candidate devices include terminals, and the network performance analysis information has terminal granularity.

4. The method of claim 1 , wherein the network signal quality information comprises a signal strength indication RSSI or a route round trip time RTT.

5. The first request message includes: a region of interest; Radio access method limited information; The device determination method of claim 1 , further comprising filtering information including at least one of:

6. Before the step of the first communication device determining N devices to participate in the federated learning from the M candidate devices based on the network performance analysis information, the device determination method includes: the first communication device determining that the M candidate devices are willing to engage in federated learning; and / or The device determination method of claim 2 , further comprising: the first communication device determining that the M candidate devices are capable of federated learning.

7. The device determination method of claim 1 , wherein the first communication device includes an application function AF and the second communication device includes a network data analysis function NWDAF.

8. receiving, by the second communication device, a first request message from the first communication device for requesting to obtain network performance analysis information; the second communication device transmitting the network performance analysis information to the first communication device, wherein the network performance analysis information includes network performance analysis information corresponding to M candidate devices, where M is a positive integer; The network performance analysis information corresponding to one candidate device is Time information of the one candidate device available to participate in federated learning; time period information covered by the network of the one candidate device; and network signal quality information of the one candidate device.

9. The device determination method according to claim 8 , wherein the candidate devices include terminals, and the network performance analysis information has terminal granularity.

10. The method of claim 8 , wherein the network signal quality information comprises a signal strength indication RSSI or a route round trip time RTT.

11. The device determination method of claim 8 , wherein the first communication device includes an application function AF and the second communication device includes a network data analysis function NWDAF.

12. A communication device comprising a processor and a memory, wherein a program or command executable by the processor is stored in the memory, and when the program or command is executed by the processor, the steps of the device determination method described in any one of claims 1 to 7 are realized.

13. A communication device comprising a processor and a memory, wherein a program or command executable by the processor is stored in the memory, and when the program or command is executed by the processor, the steps of the device determination method described in any one of claims 8 to 11 are realized.

14. A readable storage medium storing a program or command that, when executed by a processor, realizes the steps of the device determination method described in any one of claims 1 to 7.

15. A readable storage medium storing a program or commands that, when executed by a processor, implements the steps of the device determination method according to any one of claims 8 to 11.

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