Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks
under Federated Learning, A Survey and Taxonomy

Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy

16 May 2024

Olivera Kotevska

Viktor Reshniak

Papers citing "Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy"

18 / 18 papers shown

Title
Client-side Gradient Inversion Against Federated Learning from Poisoning Jiaheng Wei Yanjun Zhang Leo Yu Zhang Chao Chen Shirui Pan Kok-Leong Ong Jinchao Zhang Yang Xiang AAML 51 3 0 14 Sep 2023
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning Shenghui Li Edith C.H. Ngai Thiemo Voigt FedML AAML 45 56 0 14 Feb 2023
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis Sanjay Kariyappa Chuan Guo Kiwan Maeng Wenjie Xiong G. E. Suh Moinuddin K. Qureshi Hsien-Hsin S. Lee FedML 74 29 0 12 Sep 2022
Recovering Private Text in Federated Learning of Language Models Samyak Gupta Yangsibo Huang Zexuan Zhong Tianyu Gao Kai Li Danqi Chen FedML 63 78 0 17 May 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 168 96 0 01 Feb 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 95 184 0 06 Dec 2021
Understanding Training-Data Leakage from Gradients in Neural Networks for Image Classification Cangxiong Chen Neill D. F. Campbell FedML 44 24 0 19 Nov 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 75 64 0 10 Jun 2021
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 54 136 0 15 Oct 2020
InstaHide: Instance-hiding Schemes for Private Distributed Learning Yangsibo Huang Zhao Song Keqin Li Sanjeev Arora FedML PICV 72 152 0 06 Oct 2020
Federated Learning with Compression: Unified Analysis and Sharp Guarantees Farzin Haddadpour Mohammad Mahdi Kamani Aryan Mokhtari M. Mahdavi FedML 71 277 0 02 Jul 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 98 1,223 0 31 Mar 2020
A Theory of Usable Information Under Computational Constraints Yilun Xu Shengjia Zhao Jiaming Song Russell Stewart Stefano Ermon 70 173 0 25 Feb 2020
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 110 1,612 0 01 Nov 2019
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Y. Zhang Phillip Isola Alexei A. Efros Eli Shechtman Oliver Wang EGVM 355 11,784 0 11 Jan 2018
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning Briland Hitaj G. Ateniese Fernando Perez-Cruz FedML 113 1,401 0 24 Feb 2017
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 392 17,453 0 17 Feb 2016
Federated Optimization:Distributed Optimization Beyond the Datacenter Jakub Konecný H. B. McMahan Daniel Ramage FedML 113 737 0 11 Nov 2015