Privacy-Preserving Federated Learning Framework for Multi-Institutional Healthcare Data Analytics with Differential Privacy and Homomorphic Encryption

Abstract

Healthcare data analytics across multiple institutions faces significant privacy challenges due to regulatory requirements and data sensitivity concerns. This paper presents a comprehensive privacy-preserving federated learning framework specifically designed for multi-institutional healthcare data analytics, integrating differential privacy mechanisms with homomorphic encryption techniques. The proposed framework addresses critical limitations in existing approaches by implementing adaptive privacy budget allocation strategies and secure gradient aggregation protocols tailored for healthcare environments. The system architecture incorporates four primary components: local training nodes with privacy protection modules, secure aggregation servers, communication orchestrators, and privacy management systems. Differential privacy implementation utilizes sophisticated noise injection mechanisms with epsilon values optimized between 0.5 and 1.2, while homomorphic encryption ensures secure gradient aggregation across participating institutions. Experimental evaluation on diverse healthcare datasets containing over 2.5 million patient records demonstrates model accuracy retention exceeding 94% while maintaining rigorous privacy guarantees. Performance analysis reveals successful convergence within 85-120 training rounds with computational overhead remaining below 15% compared to centralized approaches. The framework exhibits optimal scalability for networks encompassing up to 20 healthcare entities. Privacy-utility trade-off evaluation confirms superior performance compared to existing federated learning approaches in healthcare contexts. Compliance verification demonstrates adherence to HIPAA and GDPR requirements, establishing practical feasibility for real-world healthcare implementations while advancing collaborative medical research capabilities.