Federated Learning for Healthcare Data Privacy and Security
Abstract
Federated Learning (FL) represents a revolutionary approach to achieving privacy-preserving intelligence in healthcare by enabling multi-institutional AI collaboration without sharing sensitive patient data. This study investigates FL's potential to enhance data privacy, security, and interoperability across Electronic Health Records (EHRs), medical imaging, genomics, and personalized medicine. By integrating Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, the proposed framework ensures HIPAA and GDPR compliance while maintaining model accuracy and trust. The research highlights how FL mitigates risks of data breaches, fosters ethical AI-driven healthcare ecosystems, and supports precision diagnostics, predictive analytics, and individualized treatment through secure, scalable, and decentralized data governance.
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