  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.
