A Privacy-Preserving Personalized Federated Learning Framework with Byzantine Robustness for Healthcare Data

Abstract

Federated Learning (FL) enables multiple entities to collaboratively train models without sharing sensitive data, but it faces critical privacy, security, and efficiency challenges in healthcare intrusion detection systems. These issues are intensified by adversarial attacks, non-IID data, and the need for real-time performance. Existing FL methods struggle with gradient inversion, model poisoning, Sybil attacks, and high computational overhead, limiting their effectiveness in secure and scalable healthcare applications. This work proposes the PrivacyPreserving Personalized Federated Learning Intrusion Detection in Healthcare applications (P3FL-HIDS), integrating Byzantinerobust aggregation, gradient masking, and Zero-Knowledge Proof based authentication. Key features include strong adversarial resilience, protection of privacy against gradient inversion, personalized model adaptation for heterogeneous data, and secure participant authentication. Additional contributions include a dual-network training approach, adaptive clustering for personalization, and optimized secure communication for real-time healthcare scenarios. Experimental results on a Brain Tumor magnetic resonance imaging (MRI) dataset show that P3FLHIDS outperforms state of the art works in terms of accuracy, resilience, and resistance.