Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks

30 January 2024

Papers citing "Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks"

21 / 21 papers shown

Title
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 188 97 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 97 186 0 06 Dec 2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao Song Kai Li Sanjeev Arora FedML AAML SILM 73 276 0 30 Nov 2021
Gradient Inversion with Generative Image Prior Jinwoo Jeon Jaechang Kim Kangwook Lee Sewoong Oh Jungseul Ok 65 157 0 28 Oct 2021
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training Chia-Yu Chen Jiamin Ni Songtao Lu Xiaodong Cui Pin-Yu Chen ... Naigang Wang Swagath Venkataramani Vijayalakshmi Srinivasan Wei Zhang K. Gopalakrishnan 62 67 0 21 Apr 2021
Privacy-preserving Collaborative Learning with Automatic Transformation Search Wei Gao Shangwei Guo Tianwei Zhang Han Qiu Yonggang Wen Yang Liu 87 47 0 25 Nov 2020
R-GAP: Recursive Gradient Attack on Privacy Junyi Zhu Matthew Blaschko FedML 54 137 0 15 Oct 2020
FLAME: Differentially Private Federated Learning in the Shuffle Model Ruixuan Liu Yang Cao Hong Chen Ruoyang Guo Masatoshi Yoshikawa FedML 75 95 0 17 Sep 2020
SAPAG: A Self-Adaptive Privacy Attack From Gradients Yijue Wang Jieren Deng Danyi Guo Chenghong Wang Xianrui Meng Hang Liu Caiwen Ding Sanguthevar Rajasekaran 40 36 0 14 Sep 2020
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy Lichao Sun Jianwei Qian Xun Chen FedML 71 212 0 31 Jul 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 33 64 0 20 Jun 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 111 1,235 0 31 Mar 2020
iDLG: Improved Deep Leakage from Gradients Bo Zhao Konda Reddy Mopuri Hakan Bilen FedML 71 643 0 08 Jan 2020
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion Hongxu Yin Pavlo Molchanov Zhizhong Li J. Álvarez Arun Mallya Derek Hoiem N. Jha Jan Kautz 81 569 0 18 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 266 6,285 0 10 Dec 2019
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 127 1,624 0 01 Nov 2019
Differentially Private Model Publishing for Deep Learning Lei Yu Ling Liu C. Pu Mehmet Emre Gursoy Stacey Truex FedML 71 265 0 03 Apr 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes Sai Praneeth Karimireddy Quentin Rebjock Sebastian U. Stich Martin Jaggi 69 503 0 28 Jan 2019
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Richard Y. Zhang Phillip Isola Alexei A. Efros Eli Shechtman Oliver Wang EGVM 384 11,905 0 11 Jan 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 148 1,409 0 05 Dec 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 71 1,032 0 23 May 2017