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

## TL;DR

This paper introduces federated learning as a privacy-preserving, decentralized machine learning approach suitable for wireless communications, especially 5G, discussing its applications, challenges, and future research directions.

## Contribution

It provides an accessible overview of federated learning tailored for wireless communications and highlights key challenges and open problems in this domain.

## Key findings

- Federated learning offers privacy benefits in wireless data processing.
- Potential applications include 5G network optimization and management.
- Significant technical challenges remain for practical deployment.

## Abstract

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.