Inverting Gradients -- How easy is it to break privacy in federated learning?

31 March 2020

Papers citing "Inverting Gradients -- How easy is it to break privacy in federated learning?"

50 / 177 papers shown

Title
Feature Matching Data Synthesis for Non-IID Federated Learning Zijian Li Yuchang Sun Jiawei Shao Yuyi Mao Jessie Hui Wang Jun Zhang 26 19 0 09 Aug 2023
FLIPS: Federated Learning using Intelligent Participant Selection R. Bhope K. R. Jayaram N. Venkatasubramanian Ashish Verma Gegi Thomas FedML 17 3 0 07 Aug 2023
Enhanced Security with Encrypted Vision Transformer in Federated Learning Rei Aso Sayaka Shiota Hitoshi Kiya FedML 24 2 0 01 Aug 2023
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation Tomàs Ortega Hamid Jafarkhani FedML 23 6 0 01 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 65 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 30 13 0 27 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 30 23 0 20 Jul 2023
Information-Theoretically Private Federated Submodel Learning with Storage Constrained Databases Sajani Vithana S. Ulukus FedML 10 0 0 12 Jul 2023
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey Dongqi Fu Wenxuan Bao Ross Maciejewski Hanghang Tong Jingrui He 24 9 0 10 Jul 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 33 11 0 08 Jul 2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses G. Buzaglo Niv Haim Gilad Yehudai Gal Vardi Yakir Oz Yaniv Nikankin Michal Irani 26 10 0 04 Jul 2023
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning Cheng Yang Xue Yang Dongxian Wu Xiaohu Tang FedML 24 0 0 21 Jun 2023
Theoretically Principled Federated Learning for Balancing Privacy and Utility Xiaojin Zhang Wenjie Li Kai Chen Shutao Xia Qian Yang FedML 11 9 0 24 May 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
A Game-theoretic Framework for Privacy-preserving Federated Learning Xiaojin Zhang Lixin Fan Si-Yi Wang Wenjie Li Kai Chen Qiang Yang FedML 21 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 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
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
A Survey on Class Imbalance in Federated Learning Jing Zhang Chuanwen Li Jianzgong Qi Jiayuan He FedML 39 13 0 21 Mar 2023
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market Naibo Wang Wen-Yu Feng Fusheng Liu Moming Duan See-Kiong Ng FedML 15 6 0 23 Feb 2023
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning Van Tuan Tran Huy Hieu Pham Kok-Seng Wong FedML 25 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 76 47 0 21 Feb 2023
Speech Privacy Leakage from Shared Gradients in Distributed Learning Zhuohang Li Jiaxin Zhang Jian-Dong Liu FedML 14 1 0 21 Feb 2023
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment Tayyebeh Jahani-Nezhad M. Maddah-ali Giuseppe Caire FedML 14 2 0 20 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 15 20 0 20 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 31 39 0 14 Feb 2023
Does Federated Learning Really Need Backpropagation? H. Feng Tianyu Pang Chao Du Wei-Neng Chen Shuicheng Yan Min-Bin Lin FedML 26 10 0 28 Jan 2023
Differentially Private Federated Clustering over Non-IID Data Yiwei Li Shuai Wang Chong-Yung Chi Tony Q. S. Quek FedML 22 12 0 03 Jan 2023
Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain Vesal Ahsani Alireza Rahimi Mehdi Letafati B. Khalaj 36 15 0 01 Jan 2023
Model Segmentation for Storage Efficient Private Federated Learning with Top $r$ Sparsification Sajani Vithana S. Ulukus FedML 21 5 0 22 Dec 2022
Differentially Private Decentralized Optimization with Relay Communication Luqing Wang Luyao Guo Shaofu Yang Xinli Shi 13 0 0 21 Dec 2022
Rate-Privacy-Storage Tradeoff in Federated Learning with Top $r$ Sparsification Sajani Vithana S. Ulukus FedML 18 5 0 19 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
Federated Learning for 5G Base Station Traffic Forecasting V. Perifanis Nikolaos Pavlidis R. Koutsiamanis P. Efraimidis AI4TS 34 41 0 28 Nov 2022
Federated Learning Attacks and Defenses: A Survey Yao Chen Yijie Gui Hong Lin Wensheng Gan Yongdong Wu FedML 36 29 0 27 Nov 2022
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges Madhura Joshi Ankit Pal Malaikannan Sankarasubbu OOD AI4CE FedML 23 92 0 15 Nov 2022
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks Naoya Tezuka H. Ochiai Yuwei Sun Hiroshi Esaki AAML 22 4 0 07 Nov 2022
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs) E. Jefferson J. Liley Maeve Malone S. Reel Alba Crespi-Boixader ... Christian Cole F. Ritchie A. Daly Simon Rogers Jim Q. Smith 19 7 0 03 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 13 7 0 30 Oct 2022
Machine Unlearning of Federated Clusters Chao Pan Jin Sima Saurav Prakash Vishal Rana O. Milenkovic FedML MU 31 25 0 28 Oct 2022
Local Model Reconstruction Attacks in Federated Learning and their Uses Ilias Driouich Chuan Xu Giovanni Neglia F. Giroire Eoin Thomas AAML FedML 27 2 0 28 Oct 2022
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings Junyi Li Heng-Chiao Huang FedML 21 6 0 25 Oct 2022
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching Cangxiong Chen Neill D. F. Campbell FedML 16 1 0 20 Oct 2022
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey Huiyun Cao Wenlong Zou Yinkun Wang Ting Song Mengjun Liu AAML 39 4 0 19 Oct 2022
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class Khoa D. Doan Yingjie Lao Ping Li 34 40 0 17 Oct 2022
Federated Learning with Privacy-Preserving Ensemble Attention Distillation Xuan Gong Liangchen Song Rishi Vedula Abhishek Sharma Meng Zheng ... Arun Innanje Terrence Chen Junsong Yuan David Doermann Ziyan Wu FedML 15 27 0 16 Oct 2022
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability Zhao-quan Song Yitan Wang Zheng Yu Licheng Zhang FedML 23 28 0 15 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
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy Zhifeng Jiang Wei Wang Ruichuan Chen 38 6 0 26 Sep 2022
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning Jiaqi Wang R. Schuster Ilia Shumailov David Lie Nicolas Papernot FedML 19 3 0 22 Sep 2022