Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models

25 October 2021

Papers citing "Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models"

50 / 87 papers shown

Title
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning Francesco Diana André Nusser Chuan Xu Giovanni Neglia 22 0 0 15 May 2025
Empirical Calibration and Metric Differential Privacy in Language Models Pedro Faustini Natasha Fernandes Annabelle McIver Mark Dras 62 0 0 18 Mar 2025
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning Mingcong Xu Xiaojin Zhang Wei Chen Hai Jin FedML 43 0 0 08 Mar 2025
GRAIN: Exact Graph Reconstruction from Gradients Maria Drencheva Ivo Petrov Maximilian Baader Dimitar I. Dimitrov Martin Vechev FedML 44 0 0 03 Mar 2025
A Sample-Level Evaluation and Generative Framework for Model Inversion Attacks Haoyang Li Li Bai Qingqing Ye Haibo Hu Yaxin Xiao Huadi Zheng Jianliang Xu 64 0 0 26 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 X. Zhang Ninghui Li 92 1 0 28 Jan 2025
Gradient Inversion Attack on Graph Neural Networks Divya Anand Sinha Yezi Liu Ruijie Du Yanning Shen FedML 61 0 0 29 Nov 2024
Attribute Inference Attacks for Federated Regression Tasks Francesco Diana Othmane Marfoq Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML 163 1 0 19 Nov 2024
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses Isaac Baglin Xiatian Zhu Simon Hadfield FedML 27 1 0 05 Nov 2024
Federated Black-Box Adaptation for Semantic Segmentation Jay N. Paranjape S. Sikder S. Vedula Vishal M. Patel FedML 32 0 0 31 Oct 2024
Federated Learning Nodes Can Reconstruct Peers' Image Data Ethan Wilson Kai Yue Chau-Wai Wong H. Dai FedML 22 1 0 07 Oct 2024
Privacy Attack in Federated Learning is Not Easy: An Experimental Study Hangyu Zhu Liyuan Huang Zhenping Xie FedML 26 0 0 28 Sep 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
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem Qiongxiu Li Lixia Luo Agnese Gini Changlong Ji Zhanhao Hu Xiao-Li Li Chengfang Fang Jie Shi Xiaolin Hu FedML 29 3 0 21 Sep 2024
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective Zifan Wang Binghui Zhang Meng Pang Yuan Hong Binghui Wang FedML 41 0 0 22 Aug 2024
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning Chenfei Nie Qiang Li Yuxin Yang Yuede Ji Binghui Wang 40 1 0 29 Jul 2024
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models Shanghao Shi Md Shahedul Haque Abhijeet Parida M. Linguraru Y. T. Hou Syed Muhammad Anwar W. Lou FedML 33 3 0 13 Jul 2024
QBI: Quantile-based Bias Initialization for Efficient Private Data Reconstruction in Federated Learning Micha V. Nowak Tim P. Bott David Khachaturov Frank Puppe Adrian Krenzer Amar Hekalo FedML 19 1 0 26 Jun 2024
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning Zhibo Wang Zhiwei Chang Jiahui Hu Xiaoyi Pang Jiacheng Du Yongle Chen Kui Ren FedML 24 1 0 22 Jun 2024
Byzantine-Robust Decentralized Federated Learning Minghong Fang Zifan Zhang Hairi Prashant Khanduri Jia Liu Songtao Lu Yuchen Liu Neil Zhenqiang Gong AAML FedML OOD 38 18 0 14 Jun 2024
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning Mina Rafiei Mohammadmahdi Maheri Hamid R. Rabiee 27 0 0 01 Jun 2024
DAGER: Exact Gradient Inversion for Large Language Models Ivo Petrov Dimitar I. Dimitrov Maximilian Baader Mark Niklas Muller Martin Vechev FedML 55 3 0 24 May 2024
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy Yichuan Shi Olivera Kotevska Viktor Reshniak Abhishek Singh Ramesh Raskar AAML 37 1 0 16 May 2024
GI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge Jin Qian Kaimin Wei Yongdong Wu Jilian Zhang Jipeng Chen Huan Bao 31 1 0 06 May 2024
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models Yuxin Wen Leo Marchyok Sanghyun Hong Jonas Geiping Tom Goldstein Nicholas Carlini SILM AAML 26 9 0 01 Apr 2024
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models Shanglun Feng Florian Tramèr SILM 38 14 0 30 Mar 2024
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning Joshua C. Zhao Ahaan Dabholkar Atul Sharma Saurabh Bagchi FedML 28 2 0 26 Mar 2024
Secure Aggregation is Not Private Against Membership Inference Attacks K. Ngo Johan Ostman Giuseppe Durisi Alexandre Graell i Amat FedML 27 2 0 26 Mar 2024
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack Yinggui Wang Yuanqing Huang Jianshu Li Le Yang Kai Song Lei Wang AAML PICV 48 0 0 14 Mar 2024
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 35 0 0 12 Mar 2024
SPEAR:Exact Gradient Inversion of Batches in Federated Learning Dimitar I. Dimitrov Maximilian Baader Mark Niklas Muller Martin Vechev FedML 26 5 0 06 Mar 2024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks Sayedeh Leila Noorbakhsh Binghui Zhang Yuan Hong Binghui Wang AAML 23 8 0 04 Mar 2024
Analysis of Privacy Leakage in Federated Large Language Models Minh Nhat Vu Truc D. T. Nguyen Tre' R. Jeter My T. Thai 34 6 0 02 Mar 2024
Bounding Reconstruction Attack Success of Adversaries Without Data Priors Alexander Ziller Anneliese Riess Kristian Schwethelm Tamara T. Mueller Daniel Rueckert Georgios Kaissis MIACV AAML 36 1 0 20 Feb 2024
Data Reconstruction Attacks and Defenses: A Systematic Evaluation Sheng Liu Zihan Wang Yuxiao Chen Qi Lei AAML MIACV 59 4 0 13 Feb 2024
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off Yuecheng Li Lele Fu Tong Wang Jian Lou Bin Chen Lei Yang Zibin Zheng Zibin Zheng Chuan Chen FedML 70 4 0 10 Feb 2024
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks Lulu Xue Shengshan Hu Rui-Qing Zhao Leo Yu Zhang Shengqing Hu Lichao Sun Dezhong Yao AAML 6 2 0 30 Jan 2024
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning Jianwei Li Sheng Liu Qi Lei PILM SILM AAML 20 4 0 10 Dec 2023
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging Alexander Ziller Tamara T. Mueller Simon Stieger Leonhard F. Feiner Johannes Brandt R. Braren Daniel Rueckert Georgios Kaissis 58 1 0 05 Dec 2023
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning Tre' R. Jeter Truc D. T. Nguyen Raed Alharbi My T. Thai AAML 24 0 0 23 Nov 2023
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction Shanghao Shi Ning Wang Yang Xiao Chaoyu Zhang Yi Shi Y. T. Hou W. Lou 13 7 0 10 Nov 2023
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning Feng Wang Senem Velipasalar M. C. Gursoy 17 2 0 30 Oct 2023
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation Dzung Pham Shreyas Kulkarni Amir Houmansadr 30 0 0 29 Oct 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 44 10 0 24 Oct 2023
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning Hongsheng Hu Xuyun Zhang Z. Salcic Lichao Sun K. Choo Gillian Dobbie 11 16 0 30 Sep 2023
Understanding Deep Gradient Leakage via Inversion Influence Functions Haobo Zhang Junyuan Hong Yuyang Deng M. Mahdavi Jiayu Zhou FedML 61 6 0 22 Sep 2023
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 20 3 0 14 Sep 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 32 13 0 27 Jul 2023
Private Federated Learning with Autotuned Compression Enayat Ullah Christopher A. Choquette-Choo Peter Kairouz Sewoong Oh FedML 15 6 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 39 244 0 20 Jul 2023