  Model-free techniques, such as machine learning (ML), have recently attracted
much interest towards the 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 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 presents the recent advances in
FL-based training for physical layer design problems. Compared to CL, the
effectiveness of FL is presented in terms of communication overhead with a
slight performance loss in the learning accuracy. The design challenges, such
as model, data, and hardware complexity, are also discussed in detail along
with possible solutions.
