A Federated Learning Based Anomaly Detection Model for the Internet of Medical Things

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Source arXiv

Cryptography and Security

A Federated Learning Based Anomaly Detection Model for the Internet of Medical Things

Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare

Anomaly detection (ad) in a centralized healthcare ecosystem is often plagued by significant delays in response time with high performance overhead.

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 admodel, such as possibility of data poisoning.

We introduce a novel disease-based groupingmechanism 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 edge cloudlets.

Authors

Deepti Gupta, Olumide Kayode, Smriti Bhatt, Maanak Gupta, Ali Saman Tosun

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