With the outbreak of COVID-19, people are experiencing increasing physical and mental health issues. Therefore, personal daily healthcare and monitoring become vital for our physical and mental well being. As a combination of the Internet of Things (IoT) and healthcare services, the Internet of Medical Things (IoMT) has emerged to provide intelligent medical services. However, privacy and security concerns have deterred its wide adoption. In this article, we propose a Federated Learning Driven IoMT (FLDIoMT) framework, which aims to support flexible deployment of IoMT services and address the privacy and security issues at the same time. Also, a systematic workflow of IoMT services is proposed to show an efficient data processing and analysis scheme for specific medical applications. Moreover, we demonstrate the feasibility of the proposed FLDIoMT framework by implementing a novel sleep monitoring system called iSmile.

中文翻译：

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联邦学习驱动的安全医疗物联网


随着 COVID-19 的爆发，人们面临着越来越多的身心健康问题。因此，个人日常保健和监测对于我们的身心健康至关重要。作为物联网（IoT）和医疗保健服务的结合，医疗物联网（IoMT）应运而生，提供智能医疗服务。然而，隐私和安全问题阻碍了其广泛采用。在本文中，我们提出了联邦学习驱动的物联网（FLDIoMT）框架，旨在支持物联网服务的灵活部署，同时解决隐私和安全问题。此外，还提出了 IoMT 服务的系统工作流程，以展示针对特定医疗应用的高效数据处理和分析方案。此外，我们通过实施一种名为 iSmile 的新型睡眠监测系统来证明所提出的 FLDIoMT 框架的可行性。