Analog Compression and Communication for Federated Learning over Wireless MAC

In this paper, we consider federated learning in wireless edge networks. Transmitting stochastic gradients (SG) or deep model's parameters over a limited-bandwidth wireless channel can incur large training latency and excessive power consumption. Hence, data compressing is often used to reduce the communication overhead. However, efficient communication requires the compression algorithm to satisfy the constraints imposed by the communication medium and take advantage of its characteristics, such as over-the-air computations inherent in wireless multiple-access channels (MAC), unreliable transmission and idle nodes in the edge network, limited transmission power, and preserving the privacy of data.
To achieve these goals, we propose a novel framework based on Random Linear Coding (RLC) and develop efficient power management and channel usage techniques to manage the trade-offs between power consumption, communication bit-rate and convergence rate of federated learning over wireless MAC. We show that the proposed encoding/decoding results in an unbiased compression of SG, hence guaranteeing the convergence of the training algorithm without requiring error-feedback. Finally, through simulations, we show the superior performance of the proposed method over other existing techniques.