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"

37 / 87 papers shown

Title
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off Huiqiang Chen Tianqing Zhu Tao Zhang Wanlei Zhou Philip S. Yu FedML 29 43 0 25 Jun 2023
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings Disha Makhija Joydeep Ghosh Nhat Ho FedML 24 2 0 13 Jun 2023
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning Tanguy Marchand Regis Loeb Ulysse Marteau-Ferey Jean Ogier du Terrail Arthur Pignet FedML 42 4 0 13 Jun 2023
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs Shanshan Han Baturalp Buyukates Zijian Hu Han Jin Weizhao Jin ... Qifan Zhang Yuhui Zhang Carlee Joe-Wong Salman Avestimehr Chaoyang He SILM 21 12 0 08 Jun 2023
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning Kostadin Garov Dimitar I. Dimitrov Nikola Jovanović Martin Vechev AAML FedML 34 7 0 05 Jun 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 35 12 0 27 Mar 2023
LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation Joshua C. Zhao Atul Sharma A. Elkordy Yahya H. Ezzeldin Salman Avestimehr S. Bagchi AAML FedML 30 28 0 21 Mar 2023
Manipulating Transfer Learning for Property Inference Yulong Tian Fnu Suya Anshuman Suri Fengyuan Xu David E. Evans AAML 29 6 0 21 Mar 2023
Client-specific Property Inference against Secure Aggregation in Federated Learning Raouf Kerkouche G. Ács Mario Fritz FedML 57 9 0 07 Mar 2023
Active Membership Inference Attack under Local Differential Privacy in Federated Learning Truc D. T. Nguyen Phung Lai K. Tran Nhathai Phan My T. Thai FedML 16 18 0 24 Feb 2023
WW-FL: Secure and Private Large-Scale Federated Learning F. Marx T. Schneider Ajith Suresh Tobias Wehrle Christian Weinert Hossein Yalame FedML 17 2 0 20 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 34 17 0 03 Feb 2023
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML 17 20 0 09 Jan 2023
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics Renjie Pi Weizhong Zhang Yueqi Xie Jiahui Gao Xiaoyu Wang Sunghun Kim Qifeng Chen DD 39 26 0 20 Nov 2022
M-to-N Backdoor Paradigm: A Multi-Trigger and Multi-Target Attack to Deep Learning Models Linshan Hou Zhongyun Hua Yuhong Li Yifeng Zheng Leo Yu Zhang AAML 18 2 0 03 Nov 2022
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning Ruihan Wu Xiangyu Chen Chuan Guo Kilian Q. Weinberger FedML 12 26 0 19 Oct 2022
ScionFL: Efficient and Robust Secure Quantized Aggregation Y. Ben-Itzhak Helen Mollering Benny Pinkas T. Schneider Ajith Suresh Oleksandr Tkachenko S. Vargaftik Christian Weinert Hossein Yalame Avishay Yanai 30 6 0 13 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 12 22 0 06 Oct 2022
TabLeak: Tabular Data Leakage in Federated Learning Mark Vero Mislav Balunović Dimitar I. Dimitrov Martin Vechev FedML 21 7 0 04 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
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis Sanjay Kariyappa Chuan Guo Kiwan Maeng Wenjie Xiong G. E. Suh Moinuddin K. Qureshi Hsien-Hsin S. Lee FedML 13 29 0 12 Sep 2022
Accelerated Federated Learning with Decoupled Adaptive Optimization Jiayin Jin Jiaxiang Ren Yang Zhou Lingjuan Lyu Ji Liu Dejing Dou AI4CE FedML 19 51 0 14 Jul 2022
Data Leakage in Federated Averaging Dimitar I. Dimitrov Mislav Balunović Nikola Konstantinov Martin Vechev FedML 19 28 0 24 Jun 2022
Decoupled Federated Learning for ASR with Non-IID Data Hanjing Zhu Jindong Wang Gaofeng Cheng Pengyuan Zhang Yonghong Yan 23 10 0 18 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 28 46 0 08 Jun 2022
Secure Federated Clustering Songze Li Sizai Hou Baturalp Buyukates A. Avestimehr FedML 23 9 0 31 May 2022
On the (In)security of Peer-to-Peer Decentralized Machine Learning Dario Pasquini Mathilde Raynal Carmela Troncoso OOD FedML 35 19 0 17 May 2022
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation Farshid Varno Marzie Saghayi Laya Rafiee Sharut Gupta Stan Matwin Mohammad Havaei FedML 34 30 0 27 Apr 2022
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning Georg Pichler Marco Romanelli L. Rey Vega Pablo Piantanida FedML 23 10 0 30 Mar 2022
Preserving Privacy and Security in Federated Learning Truc D. T. Nguyen My T. Thai FedML 16 49 0 07 Feb 2022
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models Liam H. Fowl Jonas Geiping Steven Reich Yuxin Wen Wojtek Czaja Micah Goldblum Tom Goldstein FedML 71 56 0 29 Jan 2022
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data Isha Garg M. Nagaraj Kaushik Roy FedML 18 1 0 21 Jan 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 69 181 0 06 Dec 2021
Eluding Secure Aggregation in Federated Learning via Model Inconsistency Dario Pasquini Danilo Francati G. Ateniese FedML 14 100 0 14 Nov 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 184 411 0 14 Jul 2021
RoFL: Robustness of Secure Federated Learning Hidde Lycklama Lukas Burkhalter Alexander Viand Nicolas Küchler Anwar Hithnawi FedML 24 55 0 07 Jul 2021
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 267 3,369 0 09 Mar 2020