Optimized Federated Ensemble Learning for Secure IoMT-Based Healthcare Monitoring System

The internet of medical things (IoMT) is revolutionizing healthcare by enabling real-time data collection, sharing, and analysis. However, the sensitive nature of medical data poses significant privacy challenges. This paper presents a particle swarm optimization (PSO)-based framework designed to enhance the privacy and accuracy of IoMT systems. By leveraging federated learning (FL), the framework ensures that patient data remains decentralized, minimizing the risks of unauthorized access. PSO is utilized to optimize critical FL parameters, including learning rates, aggregation strategies, and model architectures, to achieve a balance between data privacy and predictive accuracy. The integration of PSO enhances the efficiency of the FL model by dynamically fine-tuning these parameters, enabling the framework to adapt to diverse IoMT environments. Experimental evaluations demonstrate that the proposed approach significantly improves model performance while maintaining stringent privacy standards. The results highlight the potential of the PSO-based framework as a robust and scalable solution for secure and efficient IoMT systems, addressing the dual challenges of privacy protection and predictive accuracy in healthcare applications.