Towards Fair and Privacy-Preserving Federated Deep Models

Lyu, Lingjuan, Yu, Jiangshan, Nandakumar, Karthik, Li, Yitong, Ma, Xingjun, Jin, Jiong, Yu, Han and Ng, Kee Siong 2020, Towards Fair and Privacy-Preserving Federated Deep Models, IEEE Transactions on Parallel and Distributed Systems, vol. 31, no. 11, pp. 2524-2541, doi: 10.1109/tpds.2020.2996273.

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Title Towards Fair and Privacy-Preserving Federated Deep Models
Author(s) Lyu, Lingjuan
Yu, Jiangshan
Nandakumar, Karthik
Li, Yitong
Ma, XingjunORCID iD for Ma, Xingjun orcid.org/0000-0003-2099-4973
Jin, Jiong
Yu, Han
Ng, Kee Siong
Journal name IEEE Transactions on Parallel and Distributed Systems
Volume number 31
Issue number 11
Start page 2524
End page 2541
Total pages 18
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, NJ
Publication date 2020
ISSN 1045-9219
2161-9883
Keyword(s) Federated learning
Privacy-preserving
Deep learning
Fairness
Encryption
Language eng
DOI 10.1109/tpds.2020.2996273
Indigenous content off
Field of Research 0805 Distributed Computing
0803 Computer Software
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30139146

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