  Federated learning (FL) is a privacy-preserving distributed machine learning
paradigm that operates at the wireless edge. It enables clients to collaborate
on model training while keeping their data private from adversaries and the
central server. However, current FL approaches have limitations. Some rely on
secure multiparty computation, which can be vulnerable to inference attacks.
Others employ differential privacy, but this may lead to decreased test
accuracy when dealing with a large number of parties contributing small amounts
of data. To address these issues, this paper proposes a novel approach that
integrates federated learning seamlessly into the inner workings of MIMO
(Multiple-Input Multiple-Output) systems.
