Federated Learning for 6G: Applications, Challenges, and Opportunities

TL;DR

This paper explores how federated learning can be applied to 6G wireless networks, addressing key challenges and opportunities at the intersection of wireless communication and machine learning.

Contribution

It provides a comprehensive analysis of the applications, challenges, and implementation issues of federated learning in 6G wireless networks.

Findings

Identifies key requirements for FL in wireless networks

Discusses main challenges in deploying FL for 6G

Provides solutions for implementing FL techniques in wireless communications

Abstract

Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms towards the network edge. This has led to the emergence of a fast growing area, called federated learning (FL), which integrates two originally decoupled areas: wireless communication and machine learning. In this paper, we provide a comprehensive study on the applications of FL for sixth generation (6G) wireless networks. First, we discuss the key requirements in applying FL for wireless communications. Then, we focus on the motivating application of FL for wireless communications. We identify the main problems, challenges, and provide a comprehensive treatment of implementing FL techniques for wireless communications.