AI-Enhanced Federated Learning Framework for Privacy-Preserving Healthcare Data Analytics: A Multi-Institutional Approach

Abstract

The proliferation of healthcare data across distributed medical institutions presents unprecedented opportunities for advancing clinical research while simultaneously raising critical privacy concerns. This paper proposes an AI-enhanced federated learning framework that enables collaborative healthcare data analytics without compromising patient privacy. The framework integrates differential privacy mechanisms with homomorphic encryption to facilitate secure multi-institutional collaboration. Through comprehensive experimental validation using real-world healthcare datasets, this study demonstrates the framework's effectiveness in maintaining predictive accuracy while ensuring robust privacy protection. Performance evaluation across five major medical centers reveals that the proposed approach achieves 94.3% classification accuracy in disease prediction tasks while providing provable privacy guarantees with epsilon values below 1.0. The framework successfully processes distributed datasets containing over 2.3 million patient records, reducing communication overhead by 67% compared to centralized approaches. Results indicate significant improvements in computational efficiency and model convergence speed, with privacy budgets allocated adaptively based on data sensitivity. This research contributes to the advancement of privacy-preserving machine learning in healthcare, offering practical solutions for collaborative medical research while maintaining compliance with regulatory requirements such as HIPAA and GDPR.