Differentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network
Version 3 2024-10-18, 07:08
Version 2 2024-05-30, 16:19
Version 1 2022-02-04, 08:27
conference contribution
posted on 2024-10-18, 07:08 authored by Y Wan, Y Qu, L Gao, Yong XiangYong XiangDifferentially Privacy-Preserving Federated Learning Using Wasserstein Generative Adversarial Network
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Volume
2021-SeptemberPagination
1-6Location
Athens, GreeceStart date
2021-09-05End date
2021-09-08ISSN
1530-1346eISSN
2642-7389ISBN-13
9781665427449Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ISCC 2021 : Proceedings of the 2021 IEEE Symposium on Computers and CommunicationsEvent
Computers and Communications. Symposium (2021 : Athens, Greece)Publisher
IEEEPlace of publication
Piscataway, N.J.Series
IEEE Symposium on Computers and Communications ISCCUsage metrics
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