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Differentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network

Wan, Yichen, Qu, Youyang, Gao, Longxiang and Xiang, Yong 2021, Differentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network, in ISCC 2021 : Proceedings of the 2021 IEEE Symposium on Computers and Communications, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/ISCC53001.2021.9631541.

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Title Differentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network
Author(s) Wan, YichenORCID iD for Wan, Yichen orcid.org/0000-0002-2944-4647
Qu, YouyangORCID iD for Qu, Youyang orcid.org/0000-0003-3545-7863
Gao, Longxiang
Xiang, Yong
Conference name Computers and Communications. Symposium (2021 : Athens, Greece)
Conference location Athens, Greece
Conference dates 2021/09/05 - 2021/09/08
Title of proceedings ISCC 2021 : Proceedings of the 2021 IEEE Symposium on Computers and Communications
Publication date 2021
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) CORE2020 B
ISBN 9781665427449
ISSN 1530-1346
2642-7389
Language eng
DOI 10.1109/ISCC53001.2021.9631541
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30162261

Document type: Conference Paper
Collections: Faculty of Science, Engineering and Built Environment
School of Information Technology
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Created: Tue, 08 Feb 2022, 13:20:25 EST

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