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"

21 / 21 papers shown

Title
Trustworthiness of Stochastic Gradient Descent in Distributed Learning Hongyang Li Caesar Wu Mohammed Chadli Said Mammar Pascal Bouvry 56 1 0 28 Oct 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 61 4 0 13 Feb 2024
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma Jianfeng Ma FedML 37 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 39 3 0 18 Feb 2023
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing Kunjal Panchal Sunav Choudhary Nisarg Parikh Lijun Zhang Hui Guan 37 5 0 28 Nov 2022
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 36 2 0 28 Oct 2022
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability Zhao 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 47 143 0 10 Oct 2022
FedPerm: Private and Robust Federated Learning by Parameter Permutation Hamid Mozaffari Virendra J. Marathe D. Dice FedML 27 4 0 16 Aug 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 44 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 36 46 0 08 Jun 2022
A review of Federated Learning in Intrusion Detection Systems for IoT Aitor Belenguer J. Navaridas J. A. Pascual 28 15 0 26 Apr 2022
LAMP: Extracting Text from Gradients with Language Model Priors Mislav Balunović Dimitar I. Dimitrov Nikola Jovanović Martin Vechev 27 57 0 17 Feb 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 92 93 0 01 Feb 2022
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao Song Kai Li Sanjeev Arora FedML AAML SILM 31 269 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 187 412 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 32 91 0 25 Jun 2021
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix Maximilian Lam Gu-Yeon Wei David Brooks Vijay Janapa Reddi Michael Mitzenmacher FedML 15 63 0 10 Jun 2021
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 14 132 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 Chunyan Miao FedML 38 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 17 63 0 20 Jun 2020