Wireless network

Complement (music)

Phenotype

Key (lock)

Computer network

Wireless

Biology

Gene

Operating system

Distributed computing

Raw data

Paleontology

Chemistry

Complementation

Programming language

Context (archaeology)

Overhead (engineering)

Computer science

Computer security

Biochemistry

Telecommunications

IEEE Communications Magazine

There is a growing interest in the wireless communications community to complement the traditional model-driven 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. Due 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.

Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges