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Dropout is NOT All You Need to Prevent Gradient Leakage

Dropout is NOT All You Need to Prevent Gradient Leakage

12 August 2022
Daniel Scheliga
Patrick Mäder
M. Seeland
    FedML
ArXivPDFHTML

Papers citing "Dropout is NOT All You Need to Prevent Gradient Leakage"

6 / 6 papers shown
Title
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
Caspar Meijer
Jiyue Huang
Shreshtha Sharma
Elena Lazovik
Lydia Y. Chen
AI4TS
28
0
0
26 Mar 2025
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses
Isaac Baglin
Xiatian Zhu
Simon Hadfield
FedML
27
1
0
05 Nov 2024
Privacy Preserving Federated Learning with Convolutional Variational
  Bottlenecks
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Daniel Scheliga
Patrick Mäder
M. Seeland
FedML
AAML
13
5
0
08 Sep 2023
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
Xinjian Luo
Xiangqi Zhu
FedML
60
25
0
27 Apr 2020
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
282
39,170
0
01 Sep 2014
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
243
7,620
0
03 Jul 2012
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