Energy-Aware Sparse Federated Learning Framework for Secure Edge Healthcare and Consumer IoMT Devices

In today’s world, the healthcare sector faces significant challenges in terms of computational efficiency, real-time responsiveness, and data privacy. These challenges are very sensitive in the Internet of Medical Things (IoMT) environments, where there is a need for secure and timely monitoring, control, and decision-making. This paper proposes a novel framework that integrates sparse learning with federated learning to address these challenges. The proposed method improves performance by optimizing resource utilization, minimizing communication and computational overhead, and preserving the privacy of sensitive medical data achieved by transmitting only relevant model parameters. The framework is demonstrated by experimental validation using pneumonia and COVID-19 datasets, and performance analysis is compared with the traditional centralized technique, with the performance parameters including accuracy, energy consumption, and computational and communication time. These results show the potential of the framework deployment in a real-time environment, and it has potential in developing efficient, privacy-preserving, and sustainable IoMT applications.