Internet of Medical Things (IoMT) is becoming ubiquitous with a proliferation of smart medical devices and applications used in smart hospitals, smart-home based care, and nursing homes. It utilizes smart medical devices and cloud computing services along with core Internet of Things (IoT) technologies to sense patients' vital body parameters, monitor health conditions and generate multivariate data to support just-in-time health services. Mostly, this large amount of data is analyzed in centralized servers. Anomaly Detection (AD) in a centralized healthcare ecosystem is often plagued by significant delays in response time with high performance overhead. Moreover, there are inherent privacy issues associated with sending patients' personal health data to a centralized server, which may also introduce several security threats to the AD model, such as possibility of data poisoning. To overcome these issues with centralized AD models, here we propose a Federated Learning (FL) based AD model which utilizes edge cloudlets to run AD models locally without sharing patients' data. Since existing FL approaches perform aggregation on a single server which restricts the scope of FL, in this paper, we introduce a hierarchical FL that allows aggregation at different levels enabling multi-party collaboration. We introduce a novel disease-based grouping mechanism where different AD models are grouped based on specific types of diseases. Furthermore, we develop a new Federated Time Distributed (FedTimeDis) Long Short-Term Memory (LSTM) approach to train the AD model. We present a Remote Patient Monitoring (RPM) use case to demonstrate our model, and illustrate a proof-of-concept implementation using Digital Twin (DT) and edge cloudlets.
翻译:随着智能医院、智能家庭护理和疗养院使用的智能医疗装置和应用的激增,医疗用品互联网(IOMT)正在变得无处不在。它利用智能医疗装置和云计算服务以及核心互联网(IOT)技术来感知病人的重要身体参数、监测健康状况和生成多变数据以支持及时提供的保健服务。多数情况下,在中央服务器上分析大量数据。在一个集中的医疗保健生态系统中,异常检测(AD)经常受到大量延迟反应时间和高性能管理者高时使用高性能时使用的应用。此外,还存在与将病人的远程个人健康数据发送到中央服务器有关的内在隐私问题,这也可能给AD模式带来一些安全威胁,例如数据中毒的可能性。为了用集中的AD模型克服这些问题,我们在这里提议一个基于联邦学习(FLF)的基于自动模式的模型,利用边缘云在不分享病人数据的情况下在当地运行AD模型。由于现有FLT方法在单一的边缘服务器上进行汇总,限制了FLL的范围。此外,我们引入了一个等级的FLL(我们引入了一种等级的递定式的L)隐私模型,允许在不同的水平上整合一个基于新等级的多式的货币模型, 将一个基于新货币模型用于不同等级的多式的多式的IM。