FEDERATED LEARNING FOR HEALTHCARE DATA PRIVACY

Abstract

Healthcare data is highly sensitive, and its privacy is a primary concern in medical research and clinical applications. Traditional centralized machine learning approaches require data to be transferred to a central repository, raising security and privacy concerns. Federated Learning (FL) presents a decentralized approach where machine learning models are trained across multiple devices or institutions without exchanging raw data. This paper explores the application of FL in healthcare, analyzing its benefits, challenges, and potential solutions for improving data privacy while maintaining high model performance. The study discusses various federated learning architectures, security mechanisms, and real-world implementations in medical diagnostics, patient monitoring, and clinical decision support systems.