Dropout is NOT All You Need to Prevent Gradient Leakage

12 August 2022

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 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 Isaac Baglin Xiatian Zhu Simon Hadfield FedML 27 1 0 05 Nov 2024
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 Xinjian Luo Xiangqi Zhu FedML 60 25 0 27 Apr 2020
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 Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 243 7,620 0 03 Jul 2012