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Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures

Cui, L, Qu, Youyang, Xie, G, Zeng, D, Li, R, Shen, S and Yu, S 2022, Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures, IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3492-3500, doi: 10.1109/TII.2021.3107783.

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Title Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures
Author(s) Cui, L
Qu, YouyangORCID iD for Qu, Youyang orcid.org/0000-0002-2944-4647
Xie, G
Zeng, D
Li, R
Shen, S
Yu, S
Journal name IEEE Transactions on Industrial Informatics
Volume number 18
Issue number 5
Start page 3492
End page 3500
Total pages 8
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, NJ
Publication date 2022-05
ISSN 1551-3203
1941-0050
Keyword(s) Asynchronous federated learning
Differential privacy protection
IoT anomaly detection
security
Science & Technology
Technology
Automation & Control Systems
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Computer Science
Engineering
Anomaly detection
Servers
Internet of Things
Blockchains
Privacy
Collaborative work
Language eng
DOI 10.1109/TII.2021.3107783
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30155238

Document type: Journal Article
Collections: Faculty of Science, Engineering and Built Environment
School of Information Technology
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Scopus Citation Count Cited 4 times in Scopus Google Scholar Search Google Scholar
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Created: Thu, 20 Jan 2022, 14:23:19 EST

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