Abstract
The rapid emergence of the Internet of Medical Things (IoMT) has resulted in the ubiquitous home health diagnostic network. Despite these substantial advancements in recent years, cloud-based healthcare applications continue to be underutilized due to their inability to meet demanding security, privacy, and service quality standards. Amidst these advancements and their potential to collect behavioral patterns, the devices like smart fitness watches are still neglected in the healthcare system. Edge computing and federated learning (FL) have gained traction as a possible option in such situations. In this chapter, we analyze the services of edge computing and FL in medicine to evaluate the potential of intelligent processing of clinical visual data. The duo allows remote healthcare centers with limited diagnostic skills to securely benefit from multi-modal data. This study explains the motivation behind integrating FL with edge computing for IoMT and also the areas where the duo is applied for the betterment of IoMT services. Finally, this chapter elaborates on the challenges and future directions.
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