Towards Accurate Anomaly Detection in Industrial Internet-of-Things using Hierarchical Federated Learning-Reference-Cited by-同舟云学术

Towards Accurate Anomaly Detection in Industrial Internet-of-Things using Hierarchical Federated Learning

Published:2021 Issue: Volume: Page:1-1
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wang Xiaoding¹,Garg Sahil²,Lin Hui¹,Hu Jia³,Kaddoum Georges²,Piran Md. Jalil⁴,Hossain M. Shamim⁵

Affiliation:

1. College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350117, China.

2. École de technologie supérieure (ETS), Montreal, Canada.

3. University of Exeter, UK.

4. Department of Computer Science and Engineering, Sejong University, Seoul 05006, South Korea.

5. Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh 11543, Saudi Arabia, and also with the Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Funder

the Deanship of Scientific Research at King Saud University Riyadh Saudi Arabia for funding this work through the Vice Deanship of Scientific Research Chairs Chair of Pervasive and Mobile Computing.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/6702522/09409113.pdf?arnumber=9409113

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