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Mitigating Leakage in Federated Learning with Trusted Hardware

Mitigating Leakage in Federated Learning with Trusted Hardware

10 November 2020
Javad Ghareh Chamani
D. Papadopoulos
    FedML
ArXivPDFHTML

Papers citing "Mitigating Leakage in Federated Learning with Trusted Hardware"

4 / 4 papers shown
Title
A Survey of Trustworthy Federated Learning with Perspectives on
  Security, Robustness, and Privacy
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Yifei Zhang
Dun Zeng
Jinglong Luo
Zenglin Xu
Irwin King
FedML
84
48
0
21 Feb 2023
Vertical Federated Learning: Concepts, Advances and Challenges
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu
Yan Kang
Tianyuan Zou
Yanhong Pu
Yuanqin He
Xiaozhou Ye
Ye Ouyang
Yaqin Zhang
Qian Yang
FedML
64
162
0
23 Nov 2022
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
Runhua Xu
Nathalie Baracaldo
J. Joshi
32
100
0
10 Aug 2021
Slalom: Fast, Verifiable and Private Execution of Neural Networks in
  Trusted Hardware
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramèr
Dan Boneh
FedML
114
395
0
08 Jun 2018
1