Federated Learning: Challenges, Methods, and Future Directions

TL;DR

This paper reviews federated learning, highlighting its unique challenges in distributed environments, and discusses current methods and future research directions for privacy, scalability, and heterogeneity.

Contribution

It provides a comprehensive overview of federated learning's challenges, current approaches, and outlines future research directions across multiple communities.

Findings

Federated learning faces unique challenges in heterogeneity and scalability.

Current approaches address privacy and communication efficiency.

Future directions include improving robustness and theoretical understanding.

Abstract

Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.