Privacy-Preserving Federated Learning for Multi-Hospital Patient Data Integration

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Author(s): Arul Selvam P. (Hindusthan College of Engineering and Technology, India)and Tamije Selvy P. (Hindusthan College of Engineering and Technology, India)
Copyright: 2027
Pages: 30
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.ch007

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

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Abstract

The exponential growth of medical data across hospitals presents significant opportunities for predictive healthcare analytics, yet stringent privacy laws and data fragmentation hinder collaborative research. This chapter introduces a Privacy-Preserving Federated Learning (PP-FedAvg) framework that enables multiple hospitals to train machine-learning models collectively without sharing raw patient records. The system architecture integrates secure aggregation, homomorphic encryption, and differential privacy to ensure confidentiality during model updates, while blockchain-based accountability enhances transparency and trust. Experiments conducted on the MIMIC-III and MedMNIST datasets demonstrate that the proposed framework achieves near-centralized accuracy while significantly reducing privacy loss and communication cost. These results confirm that privacy-preserving federated learning can enable secure, regulation-compliant, and scalable collaboration among healthcare institutions.

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Privacy-Preserving Federated Learning for Multi-Hospital Patient Data Integration

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