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Federated learning effectively addresses issues such as data privacy bycollaborating across participating devices to train global models. However,factors such as network topology and device computing power can affect itstraining or communication process in complex network environments. A newnetwork architecture and paradigm with computing-measurable, perceptible,distributable, dispatchable, and manageable capabilities, computing and networkconvergence (CNC) of 6G networks can effectively support federated learningtraining and improve its communication efficiency. By guiding the participatingdevices' training in federated learning based on business requirements,resource load, network conditions, and arithmetic power of devices, CNC canreach this goal. In this paper, to improve the communication efficiency offederated learning in complex networks, we study the communication efficiencyoptimization of federated learning for computing and network convergence of 6Gnetworks, methods that gives decisions on its training process for differentnetwork conditions and arithmetic power of participating devices in federatedlearning. The experiments address two architectures that exist for devices infederated learning and arrange devices to participate in training based onarithmetic power while achieving optimization of communication efficiency inthe process of transferring model parameters. The results show that the methodwe proposed can (1) cope well with complex network situations (2) effectivelybalance the delay distribution of participating devices for local training (3)improve the communication efficiency during the transfer of model parameters(4) improve the resource utilization in the network.

Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks