FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments

Abstract

The advent of the Internet of Medical Things (IoMT) devices has led to a healthcare revolution, introducing a new era of smart applications driven by Artificial Intelligence (AI). These advanced technologies have greatly influenced the healthcare industry and have played a crucial role in enhancing the quality of life globally. Federated Learning (FL) has become popular as a technique to create models that can be shared universally using the vast datasets collected from IoMT devices while maintaining data privacy. However, the complex variations in IoMT environments, including diverse devices, data characteristics, and model complexities, create challenges for the straightforward application of traditional FL methods. Consequently, it is not well-suited for deployment in such contexts. This paper introduces FedCure, a personalized FL framework tailored for intelligent IoMT-based healthcare applications operating within a cloud-edge architecture. FedCure is adept at addressing the challenges within IoMT environments by employing personalized FL techniques that can effectively mitigate the impact of heterogeneity. Furthermore, the integration of edge computing technology enhances processing speed and minimizes latency in intelligent IoMT applications. Lastly, this research showcases several case studies encompassing IoMT-based applications, such as Eye Retinopathy Detection, Diabetes Monitoring, Maternal Health, Remote Health Monitoring, and Human Activity Recognition. These case studies provide a means to assess the effectiveness of the proposed FedCure framework and showcase exceptional performance with accuracy and minimal communication overhead, especially in addressing the challenges posed by heterogeneity.

Keywords

• Federated learning
• edge computing
• digital healthcare
• Internet of Medical Things (IoMT)