Abstract: The present disclosure relates to customer experience perception based on federated learning. An electronic device for a service provider in a wireless communication system, the service provider capable of implementing customer experience perception with at least one other service provider in the wireless communication system through federated learning, the electronic device comprises a processing circuit configured to: determine training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business; perform local model training by using the determined training data to obtain intermediate information related to local model training; and transmit the intermediate information to a coordinator, so that the coordinator can train a customer experience perception model by aggregating the intermediate information from the provider and at least one other provider.

Abstract: A method of resource scheduling in a computing force network, wherein the computing force network includes at least one node among which at least one forward path exists, and the method includes the following steps: for each of the at least one node, acquiring node performance indicator of the node based on resource indicators of the node and at least one influence node related to the node; for each of the at least one forward path, acquiring path performance indicator of the forward path based on the performance indicators of nodes constituting the forward path; and in response to a business requirement for the computing force network, selecting at least one optimal forward path from the at least one forward path based on the forward path performance indicators of the at least one forward path.

Abstract: The present disclosure relates to method and device for scheduling of computing force network resources and computing resources. There provides a method of resource scheduling in a computing force network, wherein the computing force network comprises at least one node among which at least one forward path exists, and the method comprises the following steps: for each of the at least one node, acquiring node performance indicator of the node based on resource indicators of the node and at least one influence node related to the node; for each of the at least one forward path, acquiring path performance indicator of the forward path based on the performance indicators of nodes constituting the forward path; and in response to a business requirement for the computing force network, selecting at least one optimal forward path from the at least one forward path based on the forward path performance indicators of the at least one forward path.

Abstract: The present disclosure relates to a blockchain-based federated learning device, method and system. An electronic device for the blockchain-based federated learning is proposed, including a processing circuit configured to acquire first federated learning related information from a federated learning node, cause verifying whether the federated learning node is able to participate in federated learning based on the first federated learning related information through blockchain, and notify the federated learning side of indication information indicating federated learning nodes that are able to participate in federated learning, so that the indicated federated learning nodes that can participate in federated learning can perform data processing based on federated learning.

Abstract: An electronic device (400) on an artificial intelligence (AI) platform side, the AI platform is capable of interacting with an application side to perform at least one AI intelligentialization process with the same application configuration, the electronic device (400) includes a processing circuitry (402) which is configured to: receive a configuration request for the at least one AI intelligentialization process from an application side, the configuration request including information indicating the application configuration; and when the application configuration is satisfied, assign the application configuration for usage by the at least one AI intelligentialization process during execution.

Abstract: Embodiments of the present disclosure provide an artificial intelligence-based data processing method and apparatus, and relate to the technical field of a computing power network. The method includes: in response to at least one task request for a target application, acquiring at least one task respectively corresponding to the at least one task request; determining a second cluster respectively corresponding to each task from at least one first cluster corresponding to the target application, based on task information respectively corresponding to each task; and for each task, allocating the task to the second cluster corresponding to the task so that the second cluster performs the task based on a calculation computing power resource.

Abstract: The present disclosure relates to customer experience perception based on federated learning. An electronic device for a service provider in a wireless communication system, the service provider capable of implementing customer experience perception with at least one other service provider in the wireless communication system through federated learning, the electronic device comprises a processing circuit configured to: determine training data for training a global model related to the customer experience perception based on the federated learning, wherein the training data is related to the customer's experience for a specific service/product/business; perform local model training by using the determined training data to obtain intermediate information related to local model training; and transmit the intermediate information to a coordinator, so that the coordinator can train a customer experience perception model by aggregating the intermediate information from the provider and at least one other provider.

Abstract: The present disclosure relates to federated learning in a telecom system. For a service request from a service requester, an intelligent service provider serves as a master intelligent service provider to perform federated learning in coordination with at least one additional intelligent service provider as slave intelligent service provider, and then merge the service-related information obtained from the at least one slave intelligent service and the service-related information of the master intelligent service provider so as to provide intelligent service for the service requester, or provide merged service-related information to slave intelligent service provider.

Abstract: The present disclosure relates to federated learning in a telecom system. For a service request from a service requester, an intelligent service provider serves as a master intelligent service provider to perform federated learning in coordination with at least one additional intelligent service provider as slave intelligent service provider, and then merge the service-related information obtained from the at least one slave intelligent service and the service-related information of the master intelligent service provider so as to provide intelligent service for the service requester, or provide merged service-related information to slave intelligent service provider.

Abstract: An electronic device (400) on an artificial intelligence (AI) platform side, the AI platform is capable of interacting with an application side to perform at least one AI intelligentialization process with the same application configuration, the electronic device (400) includes a processing circuitry (402) which is configured to: receive a configuration request for the at least one AI intelligentialization process from an application side, the configuration request including information indicating the application configuration; and when the application configuration is satisfied, assign the application configuration for usage by the at least one AI intelligentialization process during execution.