How to democratise and protect AI: fair and differentially private decentralised deep learning

Lyu, Lingjuan, Li, Yitong, Nandakumar, Karthik, Yu, Jiangshan and Ma, Xingjun 2020, How to democratise and protect AI: fair and differentially private decentralised deep learning, IEEE transactions on dependable and secure computing, pp. 1-14, doi: 10.1109/tdsc.2020.3006287.

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Title How to democratise and protect AI: fair and differentially private decentralised deep learning
Author(s) Lyu, Lingjuan
Li, Yitong
Nandakumar, Karthik
Yu, Jiangshan
Ma, XingjunORCID iD for Ma, Xingjun orcid.org/0000-0003-2099-4973
Journal name IEEE transactions on dependable and secure computing
Start page 1
End page 14
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2020
ISSN 1545-5971
2160-9209
Keyword(s) Decentralised deep learning
Fairness
Credibility
Privacy
Notes Early Access Article
Language eng
DOI 10.1109/tdsc.2020.3006287
Indigenous content off
Field of Research 0803 Computer Software
0804 Data Format
0805 Distributed Computing
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30140085

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