SECUREHEALTH: EDGE-ENHANCED FEDERATED ARCHITECTURE WITH ZERO-TRUST FRAMEWORK FOR PRIVACY-PRESERVED PATIENT MONITORING

This article introduces a novel architectural framework that addresses the critical challenges of privacy, security, and real-time monitoring in modern healthcare systems.The proposed Edge-Enhanced Federated Cloud Architecture (EEFCA) integrates edge computing capabilities with federated learning principles, underpinned by a comprehensive zero-trust security framework.By processing sensitive health data at the edge and implementing federated learning for distributed model training, the architecture significantly reduces data exposure while maintaining analytical capabilities.The zero-trust framework ensures continuous verification of access requests, establishing a robust security posture across all architectural layers.Implementation in a real-world remote patient monitoring scenario demonstrates the architecture's effectiveness in reducing latency while enhancing privacy preservation and regulatory compliance.Comparative analysis against traditional cloud-based solutions reveals substantial improvements in both performance and security metrics.The proposed framework provides a scalable and secure foundation for next-generation healthcare systems, effectively balancing the demands of real-time monitoring with stringent privacy requirements.This article contributes to the growing body of knowledge in secure healthcare systems, offering practical insights for implementing privacy-preserved patient monitoring at scale.