Revolutionizing Healthcare with Federated Learning: A Comprehensive Review

Federated learning has become a game-changing paradigm in healthcare, offering secure and private federated model training across dispersed data sources. This thorough analysis examines the uses, developments, and challenges of federated learning in healthcare. Beginning with an elucidation of its fundamental principles, we examine its potential to revolutionize healthcare delivery by facilitating collaborative model training without data sharing. We survey diverse applications, including disease diagnosis, personalized treatment recommendations, drug discovery, and population health management, highlighting federated learning's impact on improving patient outcomes and healthcare efficiency. Technological advancements, such as differential privacy and secure aggregation, driving federated learning adoption, are explored. Despite its promise, federated learning faces challenges including data heterogeneity, model communication overhead, privacy concerns, and regulatory barriers. We discuss these challenges and propose solutions and future research directions. This review provides a comprehensive understanding of federated learning's role in healthcare, addressing opportunities, limitations, and future prospects.