Federated Averaging Algorithm: An Analysis of Distributed Machine Learning

Abstract

The rise of distributed machine learning has opened the door to collaborative solutions that optimize computational resources and data privacy. Among these, the Federated Averaging Algorithm (FedAvg) has emerged as a key methodology for performing decentralized optimization of machine learning models. This paper aims to explore the foundational framework of FedAvg, its practical applications, and limitations. A comprehensive understanding of FedAvg contributes to the development of more efficient and privacy-preserving machine learning algorithms.