Enhancing Federated Learning for Confidential Sensor Data Aggregation in IoMT Environments

The Internet of Medical Things (IoMT) is a trans-formative technology that enables medical systems and devices to collaborate seamlessly to improve healthcare delivery. However, the widespread adoption of IoMT raises significant privacy concerns, particularly regarding the aggregation and analysis of sensitive medical data. This paper proposes a novel approach to address these challenges through the utilization of Federated Learning (FL) techniques. We present a comprehensive framework for privacy-preserving data aggregation, leveraging FL to collaboratively train machine learning models across distributed devices while preserving data privacy and security. Our approach decentralizes the model training process and performs computations locally on edge devices. This ensures that sensitive patient data remains secure and never leaves the respective devices, eliminating the need to share data with a central server. The central server combines the weights of the parameters and transmits only the updated weights to each device. Furthermore, we conducted experimental evaluations to demonstrate the effectiveness and efficiency of our proposed approach by achieving high accuracy (90.91%) while ensuring privacy. Additionally, we compared the model with other models and related work to evaluate its performance in terms of accuracy, privacy preservation, and overall effectiveness in securing IoMT data. Overall, our work contributes to the advancement of privacy-enhancing technologies for IoMT, paving the way for more secure and trustworthy healthcare systems in the era of connected medical devices.