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. Attached Files Name Description MIMEType Size Downloads Title Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures Author(s) Cui, L 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 Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 08 Sep 2021, 08:28:07 EST Wed, 08 Sep 2021, 15:59:46 EST Thu, 20 Jan 2022, 14:23:19 EST Thu, 20 Jan 2022, 16:36:04 EST Sat, 26 Feb 2022, 01:30:46 EST Thu, 10 Mar 2022, 11:56:29 EST Thu, 10 Mar 2022, 14:04:02 EST Thu, 10 Mar 2022, 15:51:06 EST Thu, 10 Mar 2022, 16:00:09 EST Wed, 20 Apr 2022, 13:30:00 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 57 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Thu, 20 Jan 2022, 14:23:19 EST