SECUREHEALTH: A DECENTRALIZED FEDERATED LEARNING FRAMEWORK WITH BLOCKCHAIN INTEGRATION FOR PRIVACY-PRESERVING HEALTHCARE ANALYTICS

Abstract

This article presents a novel decentralized artificial intelligence platform that addresses the critical challenges of implementing machine learning in healthcare while maintaining patient privacy and regulatory compliance. The proposed architecture integrates federated learning with blockchain technology to enable healthcare institutions to train AI models locally while preserving sensitive patient data within their premises. The article demonstrates how distributed learning can be achieved without centralizing patient information, while blockchain integration ensures transparent and immutable logging of model updates. The article framework supports various healthcare applications, including clinical trials, personalized patient care, claims management, and IoT device integration. Experimental results indicate that the proposed system successfully maintains data privacy and regulatory compliance while achieving comparable performance to centralized approaches. This article contributes to the growing body of knowledge on privacy-preserving AI implementations in healthcare and offers a practical solution to the industry's data security challenges.