  There is a growing interest in the wireless communications community to
complement the traditional model-based design approaches with data-driven
machine learning (ML)-based solutions. While conventional ML approaches rely on
the assumption of having the data and processing heads in a central entity,
this is not always feasible in wireless communications applications because of
the inaccessibility of private data and large communication overhead required
to transmit raw data to central ML processors. As a result, decentralized ML
approaches that keep the data where it is generated are much more appealing.
Owing to its privacy-preserving nature, federated learning is particularly
relevant for many wireless applications, especially in the context of fifth
generation (5G) networks. In this article, we provide an accessible
introduction to the general idea of federated learning, discuss several
possible applications in 5G networks, and describe key technical challenges and
open problems for future research on federated learning in the context of
wireless communications.
