  Federated learning effectively addresses issues such as data privacy by
collaborating across participating devices to train global models. However,
factors such as network topology and device computing power can affect its
training or communication process in complex network environments. A new
network architecture and paradigm with computing-measurable, perceptible,
distributable, dispatchable, and manageable capabilities, computing and network
convergence (CNC) of 6G networks can effectively support federated learning
training and improve its communication efficiency. By guiding the participating
devices' training in federated learning based on business requirements,
resource load, network conditions, and arithmetic power of devices, CNC can
reach this goal. In this paper, to improve the communication efficiency of
federated learning in complex networks, we study the communication efficiency
optimization of federated learning for computing and network convergence of 6G
networks, methods that gives decisions on its training process for different
network conditions and arithmetic power of participating devices in federated
learning. The experiments address two architectures that exist for devices in
federated learning and arrange devices to participate in training based on
arithmetic power while achieving optimization of communication efficiency in
the process of transferring model parameters. The results show that the method
we proposed can (1) cope well with complex network situations (2) effectively
balance 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.
