Abstract: A method for processing a data channel application list includes obtaining, by a core network device, first data channel capability information of a first terminal device; obtaining, by the core network device, second data channel capability information of a second terminal device; and generating, by the core network device, a data channel application list based on the first data channel capability information and the second data channel capability information.

Abstract: Provided are a model obtaining method and a communication device. The method includes: sending, by a first communication device, a first request to a second communication device, where the first request is used to request to obtain information of at least one third communication device, and each third communication device is able to provide model information of at least one model required by the first communication device; receiving, by the first communication device, information of at least one third communication device that is sent by the second communication device; and obtaining, by the first communication device, model information of a plurality of models based on the information of the at least one third communication device, where the plurality of models are used to derive data analytics result information.

Abstract: A data processing method includes sending, by a terminal, a seventh message, where the seventh message is used to request establishment of a second data channel, where the seventh message carries fourth information, and the fourth information is used to request or indicate establishment of an indirect data channel or establishment of a data channel in which a network-side device participates; receiving, by the terminal, an eighth message; and establishing, by the terminal, the second data channel based on the eighth message.

Abstract: A communication method includes sending, by a first terminal, description information of a target application to a second terminal; receiving, by the first terminal, response information sent by the second terminal, where the response information is determined by the second terminal based on the description information of the target application; and determining, by the first terminal based on the response information, whether to communicate with the second terminal through the target application.

Abstract: This application discloses a method and an apparatus for determining a candidate member, and a device. The method for determining a candidate member in embodiments of this application includes: receiving, by a first network element, a request message sent by a second network element, where the request message includes filter information; determining, by the first network element based on the filter information, one or more first devices as candidate member(s) able to participate in federated learning; and sending, by the first network element, a response message to the second network element, where the response message includes an identifier of the candidate member, and the filter information includes at least one of the following: a time window, an algorithm type, an accuracy threshold, a wireless access type, a signal quality requirement, a traffic range, member type information, quantity information, area information, and federated learning type information.

Abstract: A network element registration method includes sending, by a first network element, a registration request to a second network element, where the registration request is used to request registration of federated learning capability information of the first network element with the second network element. The federated learning capability information of the first network element includes at least one of the following type information of federated learning training supported by the first network element, time information of federated learning training supported by the first network element, or metadata information possessed by the first network element.

Abstract: A network element registration method includes sending, by a first network element, a network element registration request to a second network element, where the network element registration request is used to register information of the first network element to the second network element, and the information of the first network element includes model training related capability information of the first network element, where the model training related capability information of the first network element includes at least one of the following: model platform information supported by the first network element; model description method information supported by the first network element; vendor information of the first network element; network element object information for which a model of the first network element can be shared; first indication information; model accuracy information reachable by the first network element; or model training speed information of the first network element.

Abstract: A device determining method includes sending, by a first communication device, a first request message to a second communication device, where the first request message is used for requesting to obtain network performance analytics information; and receiving, by the first communication device, the network performance analytics information from the second communication device, where the network performance analytics information includes network performance analytics information corresponding to M candidate devices. Network performance analytics information corresponding to one candidate device includes at least one of the following information: information about a time at which the one candidate device is able to participate in federated learning; information about a time period in which the one candidate device has network coverage; or network signal quality information of the one candidate device.

Abstract: A model accuracy determining method includes performing, by a first network element, inference on a task based on a first model; determining, by the first network element, first accuracy corresponding to the first model. The first accuracy is used to indicate accuracy of the first model on an inference result of the task; and in a case that the first accuracy reaches a preset condition, sending, by the first network element, first information to a second network element, where the first information is used to indicate that accuracy of the first model does not meet an accuracy requirement or decreases. The second network element is a network element that provides the first model.

Abstract: A method for determining model accuracy includes the first network element determines first accuracy of a first model, where the first accuracy indicates an accuracy of a result of inference performed on a task by using the first model; and sends first information to a second network element in a case that the first network element determines that the first accuracy meets a preset condition, where the first information indicates that accuracy of the first model degrades. The first network element is a network element providing the first model, and the second network element is a network element that performs inference on the task.

Abstract: A method for processing data in a communication network includes determining, by a first network element, a first accuracy of a first model. The first accuracy is used for indicating accuracy of the first model in practical inference; and re-training the first model or re-selecting a second model, by the first network element, in a case that the first accuracy meets a preset condition.

Abstract: This application discloses a model accuracy determining method and apparatus, and a network-side device. A model accuracy determining method in an embodiment of this application includes: performing, by a first network element, inference for a task based on a first model; determining, by the first network element, first accuracy corresponding to the first model, where the first accuracy is used to indicate accuracy of an inference result of the task obtained by the first model; and in a case that the first accuracy meets a preset condition, sending, by the first network element, first information to a second network element, where the first information is used to indicate that accuracy of the first model does not meet an accuracy requirement or has decreased; where the second network element is a network element that triggers the task.

Abstract: A model training method includes transmitting, by a first network element, a federated model training request message to at least one second network element when performing a federated model training process corresponding to a model training task, where the at least one second network element is a network element participating in the federated model training process, and samples corresponding to training data used by different second network elements for the federated model training process are different but have same sample features; receiving, by the first network element, first information transmitted by the at least one second network element; and performing, by the first network element, model training based on a first model and the first training result reported by the at least one second network element, to obtain a target model and/or a second training result.

Abstract: The present invention discloses a processing method of address-associated data, a user terminal and two servers, the method includes: transmitting a request for an electronic map to a map server (the first server); receiving the electronic map returned by the map server; selecting a region on the electronic map; transmitting a request for calculation of address-associated data on the region to an applied server (the second server); receiving a calculation result of the address-associated data on the region returned by the second server; and displaying the calculation result of the address-associated data on the region with the electronic map. This design and its implementation have significantly improved service response rate of processing and map-presenting massive address-associated data, hereby offered better user experience.