Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare
Open accessAbstract
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.
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A hierarchical federated learning approach permits aggregation at varying levels, facilitating collaboration among multiple parties.
“Hence few works concentrated on the development of a hierarchical FL approach that permits aggregation at varying levels, facilitating collaboration among multiple parties [46,47,48].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [46]
Gupta et al. proposed a hierarchical federated learning approach for IoMT allowing data aggregation at different levels enabling multiple healthcare organisations to collaborate securely, with grouping based on disease groups and FEDTIMEDIS LSTM for anomaly detection.
“Gupta et al. [46] proposed a hierarchical FL approach for the Internet of Medical Things (IoMT) application which allows data aggression at different levels enabling multiple healthcare organisations to collaborate with each other securely. A novel approach of grouping is proposed where the data is grouped based on different disease groups. The authors also developed a novel technique, called”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [46]
Hierarchical approaches are effective and relevant for enhancing privacy preservation in FL systems because they provide additional layers of control over data sharing among clients, edge and cloud servers.
“These approaches are effective and relevant for enhancing privacy preservation in FL systems as they provide additional layers of control over data sharing among clients, edge and cloud servers.”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [46]
By leveraging hierarchical structures to organize clients and servers, privacy-preserving collaboration is facilitated.
“By leveraging hierarchical structures to organize clients and servers, privacy-preserving collaboration is facilitated [46,47,48].”
From A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems (2023) · cites this paper as [46]
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References 37 · Cited by 139
- Authors
- Gupta, D., Kayode, O., Bhatt, S., Gupta, M., & Tosun, A. S.
- Affiliations
- The University of Texas at San Antonio (US)
- Frostburg State University (US)
- Purdue University West Lafayette (US)
- Tennessee Technological University (US)
- University of North Carolina at Pembroke,Dept. of Math. and Comp. Sci.,Pembroke,NC,USA,28372
- Venue
- 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)
- Publisher
- IEEE
- Published
- 2021-12
- Pages
- 16-25
- OpenAlex
- W3217570006
- Cited by
- 139
- References
- 37
- Citations by year
- 2026: 20 · 2025: 48 · 2024: 34 · 2023: 20 · 2022: 16 · 2021: 1
- FWCI
- 8.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Collaboration and Internet Computing (CIC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- hierarchical federated learning, anomaly detection, digital twin, smart healthcare, Internet of Medical Things, remote patient monitoring, data privacy, data poisoning, LSTM
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