Communication Efficiency in Federated Learning: Achievements and Challenges

TL;DR

This paper surveys methods to improve communication efficiency in federated learning, addressing the critical challenge of communication bottlenecks in distributed environments with privacy constraints.

Contribution

It provides a comprehensive overview of recent research efforts aimed at reducing communication costs in federated learning systems.

Findings

Various techniques for communication reduction are discussed.

Trade-offs between communication efficiency and model accuracy are analyzed.

Future research directions are identified.

Abstract

Federated Learning (FL) is known to perform Machine Learning tasks in a distributed manner. Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges. As with the emerging of any new technology, there are going to be challenges and benefits. A challenge that exists in FL is the communication costs, as FL takes place in a distributed environment where devices connected over the network have to constantly share their updates this can create a communication bottleneck. In this paper, we present a survey of the research that is performed to overcome the communication constraints in an FL setting.