Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in
Federated Learning

Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning

19 October 2022

Ruihan Wu

Kilian Q. Weinberger

Papers citing "Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning"

18 / 18 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 39 0 0 26 Mar 2025
Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation Shiyi Jiang F. Firouzi Krishnendu Chakrabarty AAML MedIm 43 0 0 19 Mar 2025
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges Qiongxiu Li Wenrui Yu Yufei Xia Jun Pang FedML 60 1 0 10 Mar 2025
Stealing Training Data from Large Language Models in Decentralized Training through Activation Inversion Attack Chenxi Dai Lin Lu Pan Zhou 50 0 0 22 Feb 2025
Privacy Attack in Federated Learning is Not Easy: An Experimental Study Hangyu Zhu Liyuan Huang Zhenping Xie FedML 26 0 0 28 Sep 2024
The poison of dimensionality Lê-Nguyên Hoang 31 2 0 25 Sep 2024
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning Kangyang Luo Shuai Wang Y. Fu Renrong Shao Xiang Li Yunshi Lan Ming Gao Jinlong Shu FedML 41 2 0 12 Sep 2024
Analyzing Inference Privacy Risks Through Gradients in Machine Learning Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino K. Parsons Bradley Malin Ye Wang FedML 38 1 0 29 Aug 2024
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective Zifan Wang Binghui Zhang Meng Pang Yuan Hong Binghui Wang FedML 41 0 0 22 Aug 2024
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation Nikolas Koutsoubis Yasin Yilmaz Ravi P. Ramachandran M. Schabath Ghulam Rasool 34 8 0 18 Jun 2024
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients Weijun Li Qiongkai Xu Mark Dras PILM 32 1 0 03 Jun 2024
AI Risk Management Should Incorporate Both Safety and Security Xiangyu Qi Yangsibo Huang Yi Zeng Edoardo Debenedetti Jonas Geiping ... Chaowei Xiao Bo-wen Li Dawn Song Peter Henderson Prateek Mittal AAML 51 11 0 29 May 2024
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy Yichuan Shi Olivera Kotevska Viktor Reshniak Abhishek Singh Ramesh Raskar AAML 43 1 0 16 May 2024
Security and Privacy Challenges of Large Language Models: A Survey B. Das M. H. Amini Yanzhao Wu PILM ELM 19 103 0 30 Jan 2024
GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks? Yu Sun Gaojian Xiong Xianxun Yao Kailang Ma Jian Cui 24 3 0 22 Jan 2024
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning Kostadin Garov Dimitar I. Dimitrov Nikola Jovanović Martin Vechev AAML FedML 36 7 0 05 Jun 2023
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H. Fowl Jonas Geiping Steven Reich Yuxin Wen Wojtek Czaja Micah Goldblum Tom Goldstein FedML 73 56 0 29 Jan 2022
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