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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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A Secure Federated Learning Framework for Privacy-Preserving Distributed Healthcare Systems

Vijay Birchha
Assistant Professor, Senior Grade-1, School of Computer Science Engineering and Artificial Intelligence, India.
S Veena
Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, India.
S Subi
Assistant Professor, Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology , India .

Keywords: Federated Learning, Privacy-Preserving AI, Differential Privacy, Homomorphic Encryption, Secure Multiparty Computation (SMPC), Non-IID Healthcare Data, Edge Computing in Healthcare.

Abstract

The proliferation of electronic health records, wearable devices, and IoT-enabled diagnostic systems has created vast opportunities for machine learning in healthcare. However, centralized data processing poses critical privacy, security, and regulatory challenges. This paper presents a secure, scalable federated learning (FL) framework tailored for distributed healthcare environments, addressing data heterogeneity, communication bottlenecks, and privacy vulnerabilities. The proposed system integrates homomorphic encryption, secure multiparty computation, and local differential privacy to safeguard patient data throughout the model training pipeline. Additionally, a personalized aggregation mechanism is employed to enhance model accuracy in non-IID data settings, while edge-aware optimization ensures reduced bandwidth consumption. Experimental evaluations using MIMIC-III and COVIDx datasets demonstrate a 7.7% improvement in accuracy and over 55% reduction in privacy leakage compared to baseline FL models. The results validate the framework’s applicability in real-world healthcare scenarios, offering a robust solution for privacy-preserving AI deployment in sensitive and heterogeneous environments.
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Details
Published
2022-06-21
Pages
1-12
Issue
Vol. 1 No. 2 (2022): IJRTTE - 01 - 02
Section
Articles

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