Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare-Reference-Cited by-同舟云学术

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

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC)
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Author:

Gupta Deepti¹,Kayode Olumide²,Bhatt Smriti³,Gupta Maanak⁴,Tosun Ali Saman⁵

Affiliation:

1. University of Texas at San Antonio,Dept. of Computer Science,San Antonio,TX,USA,78249

2. Frostburg State University,Dept. of Computer Science,Frostburg,MD,USA,21532

3. Purdue University,Dept. of Computer and Information Technology,West Lafayette,IN,USA,47907

4. Tennessee Technological University,Dept. of Computer Science,Cookeville,TN,USA,38505

5. University of North Carolina at Pembroke,Dept. of Math. and Comp. Sci.,Pembroke,NC,USA,28372

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9706610/9706611/09707165.pdf?arnumber=9707165

Reference37 articles.

1. The Case for VM-Based Cloudlets in Mobile Computing

2. Efficient Anomaly Detection for Smart Hospital IoT Systems

3. The future of digital health with federated learning

4. MedMon: Securing Medical Devices Through Wireless Monitoring and Anomaly Detection

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3. Computational frameworks for zoonotic disease control in Society 5.0: opportunities, challenges and future research directions;AI & SOCIETY;2024-06-19

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