Gradient-Leakage Resilient Federated Learning

2 July 2021

Papers citing "Gradient-Leakage Resilient Federated Learning"

34 / 34 papers shown

Title
Improving Efficiency in Federated Learning with Optimized Homomorphic Encryption Feiran Yang FedML 55 0 0 03 Apr 2025
Towards Resilient Federated Learning in CyberEdge Networks: Recent Advances and Future Trends Kai Li Zhengyang Zhang Azadeh Pourkabirian Wei Ni Falko Dressler Ozgur B. Akan 48 0 0 01 Apr 2025
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
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation HaoYang Li Wei Chen Xiaojin Zhang FedML 68 0 0 18 Dec 2024
Gradients Stand-in for Defending Deep Leakage in Federated Learning H. Yi H. Ren C. Hu Y. Li J. Deng Xin Xie FedML 18 0 0 11 Oct 2024
In-depth Analysis of Privacy Threats in Federated Learning for Medical Data B. Das M. H. Amini Yanzhao Wu 29 0 0 27 Sep 2024
Data Poisoning and Leakage Analysis in Federated Learning Wenqi Wei Tiansheng Huang Zachary Yahn Anoop Singhal Margaret Loper Ling Liu FedML SILM 20 0 0 19 Sep 2024
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion Xuan Liu Siqi Cai Qihua Zhou Song Guo Ruibin Li Kaiwei Lin DiffM AAML 24 1 0 07 Jul 2024
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks Yichang Xu Ming Yin Minghong Fang Neil Zhenqiang Gong OOD FedML 36 6 0 05 Mar 2024
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 23 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 Risks Analysis and Mitigation in Federated Learning for Medical Images B. Das M. H. Amini Yanzhao Wu 23 7 0 11 Nov 2023
PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning Jianhua Wang Xiaolin Chang Jelena Mivsić Vojislav B. Mivsić Zhi Chen Junchao Fan 39 2 0 25 Sep 2023
Understanding Deep Gradient Leakage via Inversion Influence Functions Haobo Zhang Junyuan Hong Yuyang Deng M. Mahdavi Jiayu Zhou FedML 50 6 0 22 Sep 2023
RAI4IoE: Responsible AI for Enabling the Internet of Energy Minhui Xue Surya Nepal Ling Liu Subbu Sethuvenkatraman Xingliang Yuan Carsten Rudolph Ruoxi Sun Greg Eisenhauer 19 4 0 20 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 30 23 0 20 Jul 2023
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives Moming Duan Qinbin Li Linshan Jiang Bingsheng He FedML 26 4 0 05 Jul 2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility Xiaojin Zhang Wenjie Li Kai Chen Shutao Xia Qian Yang FedML 9 9 0 24 May 2023
Securing Distributed SGD against Gradient Leakage Threats Wenqi Wei Ling Liu Jingya Zhou Ka-Ho Chow Yanzhao Wu FedML 16 18 0 10 May 2023
Gradient Leakage Defense with Key-Lock Module for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie Xiaoke Ma J. Ma FedML 12 2 0 06 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
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications Gaolei Li Yuanyuan Zhao Yi Li 11 13 0 01 Feb 2023
Social-Aware Clustered Federated Learning with Customized Privacy Preservation Yuntao Wang Zhou Su Yanghe Pan Tom H. Luan Ruidong Li Shui Yu FedML 18 17 0 25 Dec 2022
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
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing Kunjal Panchal Sunav Choudhary Nisarg Parikh Lijun Zhang Hui Guan 26 5 0 28 Nov 2022
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning Pretom Roy Ovi Emon Dey Nirmalya Roy A. Gangopadhyay FedML 16 4 0 22 Oct 2022
Concealing Sensitive Samples against Gradient Leakage in Federated Learning Jing Wu Munawar Hayat Min Zhou Mehrtash Harandi FedML 11 9 0 13 Sep 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
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage Zhuohang Li Jiaxin Zhang Lu Liu Jian-Dong Liu FedML 14 114 0 29 Mar 2022
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges Nuria Rodríguez-Barroso Daniel Jiménez López M. V. Luzón Francisco Herrera Eugenio Martínez-Cámara FedML 29 210 0 20 Jan 2022
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey Shangwei Guo Xu Zhang Feiyu Yang Tianwei Zhang Yan Gan Tao Xiang Yang Liu FedML 18 9 0 19 Dec 2021
Bayesian Framework for Gradient Leakage Mislav Balunović Dimitar I. Dimitrov Robin Staab Martin Vechev FedML 14 41 0 08 Nov 2021
Mechanism Design in Large Games: Incentives and Privacy Michael Kearns Mallesh M. Pai Aaron Roth Jonathan R. Ullman 80 182 0 17 Jul 2012