Federated Learning for Physical Layer Design

Model-free techniques, such as machine learning (ML), have recently attracted much interest for physical layer design, e.g., symbol detection, channel estimation and beamforming. Most of these ML techniques employ centralized learning (CL) schemes and assume the availability of datasets at a parameter server (PS), demanding the transmission of data from the edge devices, such as mobile phones, to the PS. Exploiting the data generated at the edge, federated learning (FL) has been proposed recently as a distributed learning scheme, in which each device computes the model parameters and sends them to the PS for model aggregation, while the datasets are kept intact at the edge. Thus, FL is more communication-efficient and privacy-preserving than CL and applicable to the wireless communication scenarios, wherein the data are generated at the edge devices. This article discusses the recent advances in FL-based training for physical layer design problems, and identifies the related design challenges along with possible solutions to improve the performance in terms of communication overhead, model/data/hardware complexity.