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PRECAD: Privacy-Preserving and Robust Federated Learning via
  Crypto-Aided Differential Privacy

PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy

22 October 2021
Xiaolan Gu
Ming Li
Lishuang Xiong
    FedML
ArXivPDFHTML

Papers citing "PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy"

4 / 4 papers shown
Title
Training Differentially Private Models with Secure Multiparty Computation
Training Differentially Private Models with Secure Multiparty Computation
Sikha Pentyala
Davis Railsback
Ricardo Maia
Rafael Dowsley
David Melanson
Anderson C. A. Nascimento
Martine De Cock
21
14
0
05 Feb 2022
Secure Byzantine-Robust Distributed Learning via Clustering
Secure Byzantine-Robust Distributed Learning via Clustering
R. K. Velicheti
Derek Xia
Oluwasanmi Koyejo
FedML
OOD
70
19
0
06 Oct 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lingjuan Lyu
Han Yu
Xingjun Ma
Chen Chen
Lichao Sun
Jun Zhao
Qiang Yang
Philip S. Yu
FedML
183
355
0
07 Dec 2020
Prochlo: Strong Privacy for Analytics in the Crowd
Prochlo: Strong Privacy for Analytics in the Crowd
Andrea Bittau
Ulfar Erlingsson
Petros Maniatis
Ilya Mironov
A. Raghunathan
David Lie
Mitch Rudominer
Ushasree Kode
J. Tinnés
B. Seefeld
91
278
0
02 Oct 2017
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