  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.
