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Revisiting Gradient Pruning: A Dual Realization for Defending against
  Gradient Attacks

Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks

30 January 2024
Lulu Xue
Shengshan Hu
Rui-Qing Zhao
Leo Yu Zhang
Shengqing Hu
Lichao Sun
Dezhong Yao
    AAML
ArXivPDFHTML

Papers citing "Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks"

3 / 3 papers shown
Title
Fishing for User Data in Large-Batch Federated Learning via Gradient
  Magnification
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Yuxin Wen
Jonas Geiping
Liam H. Fowl
Micah Goldblum
Tom Goldstein
FedML
89
92
0
01 Feb 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private
When the Curious Abandon Honesty: Federated Learning Is Not Private
Franziska Boenisch
Adam Dziedzic
R. Schuster
Ali Shahin Shamsabadi
Ilia Shumailov
Nicolas Papernot
FedML
AAML
69
181
0
06 Dec 2021
ScaleCom: Scalable Sparsified Gradient Compression for
  Communication-Efficient Distributed Training
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Chia-Yu Chen
Jiamin Ni
Songtao Lu
Xiaodong Cui
Pin-Yu Chen
...
Naigang Wang
Swagath Venkataramani
Vijayalakshmi Srinivasan
Wei Zhang
K. Gopalakrishnan
27
18
0
21 Apr 2021
1