  Cooperative training methods for distributed machine learning typically
assume noiseless and ideal communication channels. This work studies some of
the opportunities and challenges arising from the presence of wireless
communication links. We specifically consider wireless implementations of
Federated Learning (FL) and Federated Distillation (FD), as well as of a novel
Hybrid Federated Distillation (HFD) scheme. Both digital implementations based
on separate source-channel coding and over-the-air computing implementations
based on joint source-channel coding are proposed and evaluated over Gaussian
multiple-access channels.
