Client-side Gradient Inversion Against Federated Learning from Poisoning

14 September 2023

Papers citing "Client-side Gradient Inversion Against Federated Learning from Poisoning"

7 / 7 papers shown

Title
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
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification Zirui Gong Liyue Shen Yanjun Zhang Leo Yu Zhang Jingwei Wang Guangdong Bai Yong Xiang AAML 31 6 0 13 Nov 2023
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 86 92 0 01 Feb 2022
Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks Lukas Struppek Dominik Hintersdorf Antonio De Almeida Correia Antonia Adler Kristian Kersting MIACV 60 62 0 28 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
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping Xiaoyu Cao Minghong Fang Jia Liu Neil Zhenqiang Gong FedML 117 611 0 27 Dec 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 182 1,032 0 29 Nov 2018