Federated Learning for Healthcare Data Privacy and Security: From EHRs to Personalized Medicine

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Author(s): S. Aarthi (Marwadi University, Rajkot, India)and Jaypalsinh A. Gohil (Marwadi University, Rajkot, India)
Copyright: 2027
Pages: 40
Source title: Managing Sensitive Health Data Through Federated Learning and Generative AI: Privacy Preserving Techniques
Source Author(s)/Editor(s): Manisha Guduri (Lawrence Technological University, USA), George Pappas (Lawrence Technological University, USA)and Sandeep Thota (Oracle Inc., USA)
DOI: 10.4018/979-8-3373-7426-0.ch014

Keywords: Health Information Systems / Information Security & Privacy / Medical Information Science Reference / Security & Forensics

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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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Federated Learning for Healthcare Data Privacy and Security: From EHRs to Personalized Medicine

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