FEDERATED LEARNING FOR PRIVACYPRESERVING HEALTHCARE DATA ANALYTICS

Abstract

The rising ubiquity of electronic health records, wearable sensors and hospital imaging systems presents vast opportunities for datadriven healthcare analytics. However, concerns over patient privacy, regulatory compliance and data siloing hinder the sharing of raw medical data across institutions. Federated Learning (FL) offers a collaborative machine learning paradigm in which local models are trained at individual healthcare sites and only model updates (not raw patient data) are exchanged. This article examines the architecture of FL in healthcare settings, explores privacypreserving mechanisms (secure aggregation, differential privacy, homomorphic encryption), analyses domainspecific challenges such as nonIID medical data and communication overhead, and discusses empirical trends and deployment pathways. Two conceptual charts visualise the tradeoffs between number of sites and model accuracy, and between privacy strength and model error. We conclude by outlining key recommendations for translational deployment in multisite healthcare analytics.