Abstract

Healthcare information systems increasingly demand sophisticated machine learning capabilities while maintaining strict patient privacy requirements. This paper introduces a novel hybrid federated learning framework that integrates differential privacy mechanisms specifically designed for healthcare information systems. Our approach addresses the dual challenges of maintaining model accuracy and ensuring robust privacy protection in distributed healthcare environments. We propose a multi-tier architecture that combines local differential privacy with global privacy budgeting, enabling healthcare institutions to collaboratively train machine learning models without compromising sensitive patient data. The framework incorporates adaptive noise calibration based on data sensitivity levels and implements a dynamic participant selection mechanism to optimize both privacy and performance. Through extensive experiments on real-world healthcare datasets, our method demonstrates superior performance compared to existing approaches, achieving 94.7% accuracy on disease prediction tasks while maintaining Ɛ-differential privacy with Ɛ = 0.5. The security evaluation reveals robust resistance against various privacy attacks, including membership inference and model inversion attacks. Performance analysis shows a 23% improvement in communication efficiency and 18% reduction in training time compared to traditional federated learning approaches. Our framework provides a practical solution for healthcare organizations seeking to leverage collaborative machine learning while adhering to stringent privacy regulations such as HIPAA and GDPR.

Keywords: Federated learning, Differential privacy, Healthcare information systems, Privacy-preserving machine learning, Distributed computing, Security evaluation