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An Adaptive and Fast Convergent Approach to Differentially Private Deep
  Learning

An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning

19 December 2019
Zhiying Xu
Shuyu Shi
A. Liu
Jun Zhao
Lin Chen
    FedML
ArXivPDFHTML

Papers citing "An Adaptive and Fast Convergent Approach to Differentially Private Deep Learning"

4 / 4 papers shown
Title
Adap DP-FL: Differentially Private Federated Learning with Adaptive
  Noise
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise
Jie Fu
Zhili Chen
Xiao Han
FedML
17
28
0
29 Nov 2022
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on
  Simulated Annealing
SA-DPSGD: Differentially Private Stochastic Gradient Descent based on Simulated Annealing
Jie Fu
Zhili Chen
Xinpeng Ling
22
0
0
14 Nov 2022
Gradient Leakage Attack Resilient Deep Learning
Gradient Leakage Attack Resilient Deep Learning
Wenqi Wei
Ling Liu
SILM
PILM
AAML
22
46
0
25 Dec 2021
Efficient Per-Example Gradient Computations
Efficient Per-Example Gradient Computations
Ian Goodfellow
186
74
0
07 Oct 2015
1