Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective

8 December 2020

Jingwei Sun

Ang Li

Binghui Wang

Huanrui Yang

Hai Li

Yiran Chen

FedML

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Papers citing "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"

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Title
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling Kaiyuan Zhang Siyuan Cheng Guangyu Shen Bruno Ribeiro Shengwei An Pin-Yu Chen X. Zhang Ninghui Li 90 1 0 28 Jan 2025
Gradients Stand-in for Defending Deep Leakage in Federated Learning H. Yi H. Ren C. Hu Y. Li J. Deng Xin Xie FedML 25 0 0 11 Oct 2024
Leakage-Resilient and Carbon-Neutral Aggregation Featuring the Federated AI-enabled Critical Infrastructure Zehang Deng Ruoxi Sun Minhui Xue Sheng Wen S. Çamtepe Surya Nepal Yang Xiang 35 1 0 24 May 2024
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 25 16 0 02 Feb 2024
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective Xianghua Xie Chen Hu Hanchi Ren Jingjing Deng FedML AAML 29 19 0 27 Nov 2023
Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception? Xiaoxiao Sun Nidham Gazagnadou Vivek Sharma Lingjuan Lyu Hongdong Li Liang Zheng 39 7 0 22 Sep 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 32 23 0 20 Jul 2023
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 33 244 0 20 Jul 2023
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
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring Jeffry Wicaksana Zengqiang Yan Kwang-Ting Cheng FedML 29 5 0 01 May 2023
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense Yue-li Cui Syed Imran Ali Meerza Zhuohang Li Luyang Liu Jiaxin Zhang Jian-Dong Liu AAML FedML 21 4 0 11 Apr 2023
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties Jingwei Sun Zhixu Du Anna Dai Saleh Baghersalimi Alireza Amirshahi David Atienza Yiran Chen FedML 11 6 0 28 Mar 2023
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning Mingyuan Fan Cen Chen Chengyu Wang Ximeng Liu Wenmeng Zhou Jun Huang AAML FedML 32 0 0 05 Dec 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 22 12 0 12 Aug 2022
PASS: A Parameter Audit-based Secure and Fair Federated Learning Scheme against Free-Rider Attack Jianhua Wang Xiaolin Chang J. Misic Vojislav B. Mišić Yixiang Wang 16 7 0 15 Jul 2022
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions Rui Zhang Song Guo Junxiao Wang Xin Xie Dacheng Tao 27 36 0 15 Jun 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
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation Jeffry Wicaksana Zengqiang Yan Dong Zhang Xijie Huang Huimin Wu Xin Yang Kwang-Ting Cheng FedML 27 49 0 04 May 2022
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data Jingyu Pan Chen-Chia Chang Zhiyao Xie Ang Li Minxue Tang Tunhou Zhang Jiangkun Hu Yiran Chen FedML 19 8 0 30 Mar 2022
Bayesian Framework for Gradient Leakage Mislav Balunović Dimitar I. Dimitrov Robin Staab Martin Vechev FedML 19 41 0 08 Nov 2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 12 144 0 25 Oct 2021