Federated Learning for Internet of Medical Things (IoMT): A Secure and Scalable Approach

The Internet of Medical Things (IoMT) enables remote patient monitoring and real-time healthcare services. However, traditional centralized machine learning models pose significant privacy and security risks, Federated Learning (FL) offers a decentralized solution by allowing IOMT devices to collaboratively train models without sharing raw data. In this paper, we propose an FL-based framework for IoMT that enhances privacy, communication efficiency and scalability. The proposed model incorporates differential privacy and homomorphic encryption to ensure secure aggregation while optimizing communication costs through model compression techniques. Experimental results demonstrate improved performance in terms of accuracy, communication overhead, and privacy preservation compared to centralized learning methods.