FedRH: Federated Learning Based Remote Healthcare

Abstract

More and more people are developing chronic illnesses, which require regular visits to the hospital for treatment and monitoring. Due to the advancements in wearable computing technology, there is a growing interest in remote health monitoring worldwide. The Internet of Medical Things (IoMT) devices allow users to access their health data. Recent developments in smart healthcare have shown that machine learning model training using vast amounts of healthcare data has been remarkably successful. However, remote healthcare is facing a significant challenge as user data is stored in isolated silos, thus making data aggregation impractical while ensuring data privacy and security. To overcome this challenge, this paper presents FedRH, a federated learning framework for remote healthcare. FedRH solves the issue of data isolation through a cloud-edge-based computing architecture and FL, providing personalized healthcare without sacrificing privacy and security. Experiments show that FedRH achieves a greater accuracy of 5.6% compared to conventional approaches. FedRH is a flexible and versatile tool that can be applied to various healthcare applications, making it an ideal choice for many use cases. © 2023 IEEE.