Federated Learning for Industrial Internet of Things in Future Industries Nguyen, DC, Ding, M, Pathirana, Pubudu, Seneviratne, A, Li, J, Niyato, D and Poor, HV 2021, Federated Learning for Industrial Internet of Things in Future Industries, IEEE Wireless Communications Magazine, vol. 28, no. 6, pp. 192-199, doi: 10.1109/MWC.001.2100102. Attached Files Name Description MIMEType Size Downloads Title Federated Learning for Industrial Internet of Things in Future Industries Author(s) Nguyen, DC Ding, M Pathirana, Pubudu orcid.org/0000-0001-8014-7798 Seneviratne, A Li, J Niyato, D Poor, HV Journal name IEEE Wireless Communications Magazine Volume number 28 Issue number 6 Start page 192 End page 199 Total pages 8 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Publication date 2021-12 ISSN 1070-9916 1558-0687 Keyword(s) Artificial intelligence Computer Science Computer Science, Hardware & Architecture Computer Science, Information Systems Data models Engineering Engineering, Electrical & Electronic Industrial Internet of Things Industries Load modeling Science & Technology Servers Technology Telecommunications Training Language eng DOI 10.1109/MWC.001.2100102 Field of Research 0805 Distributed Computing 0906 Electrical and Electronic Engineering 1005 Communications Technologies HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30161375 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering Institute for Health Transformation Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version 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, 12 Jan 2022, 22:01:09 EST Wed, 12 Jan 2022, 22:01:41 EST Thu, 13 Jan 2022, 14:52:41 EST Thu, 13 Jan 2022, 15:04:36 EST Thu, 27 Jan 2022, 13:04:40 EST Thu, 27 Jan 2022, 13:06:46 EST Thu, 27 Jan 2022, 13:07:45 EST Thu, 27 Jan 2022, 16:04:47 EST Thu, 27 Jan 2022, 16:07:47 EST Thu, 27 Jan 2022, 16:11:52 EST Sat, 26 Feb 2022, 01:42:57 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 69 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 12 Jan 2022, 22:01:41 EST