Edge-Intelligent Blockchain Framework for Ultra-Secure and Energy-Efficient Real-Time Patient Monitoring in IoMT Using Hierarchical Federated Learning

Abstract

Internet of Medical Things (IoMT) provides the possibility to conduct continuous monitoring of health, perform intelligent diagnostics, and make a clinical decision based on data. Nonetheless, there are security, privacy, scalability, latency, and energy issues with large-scale deployment. Although Federated learning (FL) provides less exposure to data, and blockchain provides trust, current solutions that combine both blockchain and FL have high consensus overhead, fixed privacy, and adversarial resilience. To handle them, we present an Edge-Intelligent Hierarchical Blockchain-IoMT framework that integrates Hierarchical FL (HFL), Adaptive Differential Privacy (ADP), Lightweight Homomorphic Encryption (LHE), Zero-Knowledge Proof (ZKP) authentication, and an Energy-Aware PoS with Edge Learning (PoS-EL) consensus. Hierarchical aggregation minimizes bottlenecks in communication. ADP minimizes security vs utility. ZKP achieves authentication and PoS-EL minimizes energy consumption. Experiments on real-world data demonstrate 99.21% accuracy of detecting anomalies, 34% decreased latency, 41% decreased energy usage, 52 percent lower blockchain overhead and 97 percent resistance to adversarial attacks, which justifies the framework in real-time, mission-critical IoMT systems.