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Amplitude-Varying Perturbation for Balancing Privacy and Utility in
  Federated Learning

Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning

7 March 2023
Xinnan Yuan
W. Ni
Ming Ding
Kang Wei
Jun Li
H. Vincent Poor
    FedML
ArXivPDFHTML

Papers citing "Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning"

5 / 5 papers shown
Title
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li
Lele Fu
Tong Wang
Jian Lou
Bin Chen
Lei Yang
Zibin Zheng
Zibin Zheng
Chuan Chen
FedML
67
4
0
10 Feb 2024
Machine Learning-Aided Operations and Communications of Unmanned Aerial
  Vehicles: A Contemporary Survey
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
Harrison Kurunathan
Hailong Huang
Kai Li
Wei Ni
E. Hossain
13
70
0
07 Nov 2022
Adaptive Federated Learning in Resource Constrained Edge Computing
  Systems
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang
Tiffany Tuor
Theodoros Salonidis
K. Leung
C. Makaya
T. He
Kevin S. Chan
144
1,685
0
14 Apr 2018
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
MDE
BDL
PINN
206
14,367
0
07 Oct 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the
  Polyak-Łojasiewicz Condition
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition
Hamed Karimi
J. Nutini
Mark W. Schmidt
133
1,198
0
16 Aug 2016
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