A Framework for Evaluating Gradient Leakage Attacks in Federated
Learning

A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

22 April 2020

Mehmet Emre Gursoy

Papers citing "A Framework for Evaluating Gradient Leakage Attacks in Federated Learning"

17 / 17 papers shown

Title
Trustworthiness of Stochastic Gradient Descent in Distributed Learning Hongyang Li Caesar Wu Mohammed Chadli Said Mammar Pascal Bouvry 46 1 0 28 Oct 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 59 4 0 13 Feb 2024
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma J. Ma FedML 21 2 0 06 May 2023
Digital Privacy Under Attack: Challenges and Enablers Baobao Song Mengyue Deng Shiva Raj Pokhrel Qiujun Lan R. Doss Gang Li AAML 26 3 0 18 Feb 2023
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 27 2 0 28 Oct 2022
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability Zhao-quan Song Yitan Wang Zheng Yu Licheng Zhang FedML 23 28 0 15 Oct 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Jean Ogier du Terrail Samy Ayed Edwige Cyffers Felix Grimberg Chaoyang He ... Sai Praneeth Karimireddy Marco Lorenzi Giovanni Neglia Marc Tommasi M. Andreux FedML 36 142 0 10 Oct 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 22 12 0 12 Aug 2022
Gradient Obfuscation Gives a False Sense of Security in Federated Learning Kai Yue Richeng Jin Chau-Wai Wong D. Baron H. Dai FedML 26 46 0 08 Jun 2022
A review of Federated Learning in Intrusion Detection Systems for IoT Aitor Belenguer J. Navaridas J. A. Pascual 13 15 0 26 Apr 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 79 92 0 01 Feb 2022
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao-quan Song Kai Li Sanjeev Arora FedML AAML SILM 12 269 0 30 Nov 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 173 411 0 14 Jul 2021
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy Xinwei Zhang Xiangyi Chen Min-Fong Hong Zhiwei Steven Wu Jinfeng Yi FedML 22 90 0 25 Jun 2021
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 6 132 0 15 Oct 2020
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework Jiawen Kang Zehui Xiong Chunxiao Jiang Yi Liu Song Guo Yang Zhang Dusit Niyato Cyril Leung C. Miao FedML 25 40 0 10 Aug 2020
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks Lixin Fan Kam Woh Ng Ce Ju Tianyu Zhang Chang Liu Chee Seng Chan Qiang Yang MIACV 9 63 0 20 Jun 2020