A Novel Hybrid Federated Learning and Digital Twin Approach for Secure and Adaptive AI in Healthcare IoT

Through enhanced predictive analytics and patient management remote monitoring AI together with Internet of Things technology now drives healthcare transformation. Traditional AI models depend heavily on centralized data storage systems which produce privacy and security issues and scalability problems. The research develops a fresh fusion between Secure Digital Twin technology and Federated Learning to accomplish real-time adaptive learning while maintaining data privacy. The proposed method applies model compression for computation efficiency and block chain authentication for protected data transfers and secure MPC to boost confidentiality with reduced computational strain. By applying this framework to both the ECG PhysioNet dataset and AMIGOS multimodal wearable sensor data researchers see significant improvements. The results show a 92.3% accuracy rate alongside an 18.5% classification accuracy boost and 30% decreased communication overhead when compared to standard Federated Learning approaches. FL and DT integration enables this research to build an intelligent, scalable, privacy-protected healthcare IoT system which improves patient care and medical decision-making systems in real-time settings.