iDLG: Improved Deep Leakage from Gradients

8 January 2020

Papers citing "iDLG: Improved Deep Leakage from Gradients"

50 / 111 papers shown

Title
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning Francesco Diana André Nusser Chuan Xu Giovanni Neglia 27 0 0 15 May 2025
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments Kaixiang Zhao Lincan Li Kaize Ding Neil Zhenqiang Gong Yue Zhao Yushun Dong AAML 52 0 0 22 Feb 2025
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling Kaiyuan Zhang Siyuan Cheng Guangyu Shen Bruno Ribeiro Shengwei An Pin-Yu Chen Xinming Zhang Ninghui Li 148 1 0 28 Jan 2025
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning Nurbek Tastan Samuel Horváth Karthik Nandakumar FedML 69 0 0 21 Jan 2025
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses Isaac Baglin Xiatian Zhu Simon Hadfield FedML 32 1 0 05 Nov 2024
Gradients Stand-in for Defending Deep Leakage in Federated Learning H. Yi H. Ren C. Hu Y. Li J. Deng Xin Xie FedML 35 0 0 11 Oct 2024
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework Zilinghan Li Shilan He Ze Yang Minseok Ryu Kibaek Kim Ravi K. Madduri FedML 63 7 0 17 Sep 2024
Gradient Inversion of Federated Diffusion Models Jiyue Huang Chi Hong Lydia Y. Chen Stefanie Roos FedML 39 1 0 30 May 2024
DAGER: Exact Gradient Inversion for Large Language Models Ivo Petrov Dimitar I. Dimitrov Maximilian Baader Mark Niklas Muller Martin Vechev FedML 63 3 0 24 May 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 45 1 0 24 May 2024
On the Efficiency of Privacy Attacks in Federated Learning Nawrin Tabassum Ka-Ho Chow Xuyu Wang Wenbin Zhang Yanzhao Wu FedML 39 1 0 15 Apr 2024
You Can Use But Cannot Recognize: Preserving Visual Privacy in Deep Neural Networks Qiushi Li Yan Zhang Ju Ren Qi Li Yaoxue Zhang AAML PICV 41 23 0 05 Apr 2024
Federated Continual Learning via Knowledge Fusion: A Survey Xin Yang Hao Yu Xin Gao Hao Wang Junbo Zhang Tianrui Li FedML 38 33 0 27 Dec 2023
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx Trung-Hieu Hoang Jordan D. Fuhrman Ravi K. Madduri Miao Li Pranshu Chaturvedi ... Kibaek Kim Minseok Ryu Ryan Chard Eliu A. Huerta Maryellen L. Giger 39 5 0 14 Dec 2023
Privacy-preserving quantum federated learning via gradient hiding Changhao Li Niraj Kumar Zhixin Song Shouvanik Chakrabarti Marco Pistoia FedML 33 20 0 07 Dec 2023
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective Xianghua Xie Chen Hu Hanchi Ren Jingjing Deng FedML AAML 50 19 0 27 Nov 2023
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering Md. Rafi Ur Rashid Vishnu Asutosh Dasu Kang Gu Najrin Sultana Shagufta Mehnaz AAML FedML 46 10 0 24 Oct 2023
Text Embeddings Reveal (Almost) As Much As Text John X. Morris Volodymyr Kuleshov Vitaly Shmatikov Alexander M. Rush RALM 28 96 0 10 Oct 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 47 8 0 22 Sep 2023
Approximate and Weighted Data Reconstruction Attack in Federated Learning Yongcun Song Ziqi Wang Enrique Zuazua AAML FedML 29 4 0 13 Aug 2023
Feature Matching Data Synthesis for Non-IID Federated Learning Zijian Li Yuchang Sun Jiawei Shao Yuyi Mao Jessie Hui Wang Jun Zhang 28 20 0 09 Aug 2023
FLIPS: Federated Learning using Intelligent Participant Selection R. Bhope K.R. Jayaram N. Venkatasubramanian Ashish Verma Gegi Thomas FedML 29 3 0 07 Aug 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 75 4 0 01 Aug 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 38 13 0 27 Jul 2023
Mitigating Cross-client GANs-based Attack in Federated Learning Hong Huang Xinyu Lei Tao Xiang AAML 55 1 0 25 Jul 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 43 23 0 20 Jul 2023
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing Jinglong Luo Yehong Zhang Jiaqi Zhang Shuang Qin Haibo Wang Yue Yu Zenglin Xu 51 5 0 26 Jun 2023
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users Y. Rahulamathavan Charuka Herath Xiaolan Liu S. Lambotharan Carsten Maple 41 12 0 08 Jun 2023
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations T. Krauß Alexandra Dmitrienko AAML 27 4 0 06 Jun 2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility Xiaojin Zhang Wenjie Li Kai Chen Shutao Xia Qian Yang FedML 25 9 0 24 May 2023
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
A Game-theoretic Framework for Privacy-preserving Federated Learning Xiaojin Zhang Lixin Fan Si-Yi Wang Wenjie Li Kai Chen Qiang Yang FedML 31 4 0 11 Apr 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 34 4 0 11 Apr 2023
Secure Federated Learning against Model Poisoning Attacks via Client Filtering D. Yaldiz Tuo Zhang Salman Avestimehr AAML FedML 24 14 0 31 Mar 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 21 8 0 28 Mar 2023
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning Joshua C. Zhao A. Elkordy Atul Sharma Yahya H. Ezzeldin A. Avestimehr S. Bagchi FedML 45 12 0 27 Mar 2023
Considerations on the Theory of Training Models with Differential Privacy Marten van Dijk Phuong Ha Nguyen FedML 31 2 0 08 Mar 2023
Differentially Private Distributed Convex Optimization Minseok Ryu Kibaek Kim FedML 33 1 0 28 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 39 7 0 22 Feb 2023
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy Yifei Zhang Dun Zeng Jinglong Luo Zenglin Xu Irwin King FedML 84 48 0 21 Feb 2023
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI Juexiao Zhou Longxi Zhou Di Wang Xiaopeng Xu Haoyang Li Yuetan Chu Wenkai Han Xin Gao 30 20 0 20 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 40 39 0 14 Feb 2023
Mutual Information Regularization for Vertical Federated Learning Tianyuan Zou Yang Liu Ya-Qin Zhang AAML FedML 35 7 0 01 Jan 2023
Reconstructing Training Data from Model Gradient, Provably Zihan Wang Jason D. Lee Qi Lei FedML 32 24 0 07 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 34 0 0 05 Dec 2022
Federated Learning Attacks and Defenses: A Survey Yao Chen Yijie Gui Hong Lin Wensheng Gan Yongdong Wu FedML 44 29 0 27 Nov 2022
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing Hai M. Nguyen N. Chu Diep N. Nguyen D. Hoang Van-Dinh Nguyen Minh Hoàng Hà E. Dutkiewicz Marwan Krunz FedML 27 1 0 14 Nov 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Two Models are Better than One: Federated Learning Is Not Private For Google GBoard Next Word Prediction Mohamed Suliman D. Leith SILM FedML 26 7 0 30 Oct 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