Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective

TL;DR

This survey reviews communication-efficient strategies in federated learning, addressing the critical bottleneck of high communication costs to enable scalable, privacy-preserving decentralized machine learning applications.

Contribution

It provides a comprehensive taxonomy and analysis of state-of-the-art communication-efficient federated learning methods and discusses future research directions.

Findings

Identified key sources of communication inefficiency in FL

Reviewed various strategies to reduce communication overhead

Highlighted potential for scalable FL deployment in real-world applications

Abstract

Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data. However, the practical deployment of FL systems faces a significant bottleneck: the communication overhead caused by frequently exchanging large model updates between numerous devices and a central server. This communication inefficiency can hinder training speed, model performance, and the overall feasibility of real-world FL applications. In this survey, we investigate various strategies and advancements made in communication-efficient FL, highlighting their impact and potential to overcome the communication challenges inherent in FL systems. Specifically, we define measures for communication efficiency, analyze sources of communication inefficiency in FL systems, and provide a taxonomy and comprehensive review of state-of-the-art communication-efficient FL methods. Additionally, we discuss promising future research directions for enhancing the communication efficiency of FL systems. By addressing the communication bottleneck, FL can be effectively applied and enable scalable and practical deployment across diverse applications that require privacy-preserving, decentralized machine learning, such as IoT, healthcare, or finance.