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 / 229 papers shown

Title
Rate-Privacy-Storage Tradeoff in Federated Learning with Top $r$ Sparsification Sajani Vithana S. Ulukus FedML 26 5 0 19 Dec 2022
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
Meta-analysis of individualized treatment rules via sign-coherency Jay Jojo Cheng J. Huling Guanhua Chen 35 0 0 28 Nov 2022
Federated Learning for 5G Base Station Traffic Forecasting V. Perifanis Nikolaos Pavlidis R. Koutsiamanis P. Efraimidis AI4TS 49 42 0 28 Nov 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
How Do Input Attributes Impact the Privacy Loss in Differential Privacy? Tamara T. Mueller Stefan Kolek F. Jungmann Alexander Ziller Dmitrii Usynin Moritz Knolle Daniel Rueckert Georgios Kaissis 10 3 0 18 Nov 2022
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges Madhura Joshi Ankit Pal Malaikannan Sankarasubbu OOD AI4CE FedML 25 93 0 15 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
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks Naoya Tezuka H. Ochiai Yuwei Sun Hiroshi Esaki AAML 37 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 32 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 26 7 0 30 Oct 2022
Machine Unlearning of Federated Clusters Chao Pan Jin Sima Saurav Prakash Vishal Rana O. Milenkovic FedML MU 39 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 36 2 0 28 Oct 2022
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings Junyi Li Heng-Chiao Huang FedML 24 6 0 25 Oct 2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano Chuan Guo Alexandre Sablayrolles Maziar Sanjabi FedML 29 17 0 24 Oct 2022
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning Pretom Roy Ovi Emon Dey Nirmalya Roy A. Gangopadhyay FedML 26 4 0 22 Oct 2022
Analysing Training-Data Leakage from Gradients through Linear Systems and Gradient Matching Cangxiong Chen Neill D. F. Campbell FedML 34 1 0 20 Oct 2022
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning Ruihan Wu Xiangyu Chen Chuan Guo Kilian Q. Weinberger FedML 20 26 0 19 Oct 2022
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey Huiyun Cao Wenlong Zou Yinkun Wang Ting Song Mengjun Liu AAML 54 5 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 30 27 0 16 Oct 2022
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability Zhao 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 20 22 0 06 Oct 2022
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing Jiawei Shao Yuchang Sun Songze Li Jun Zhang OOD 44 38 0 06 Oct 2022
Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy Zhifeng Jiang Wei Wang Ruichuan Chen 43 7 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 33 3 0 22 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 23 29 0 12 Sep 2022
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation Xuan Gong Abhishek Sharma Srikrishna Karanam Ziyan Wu Terrence Chen David Doermann Arun Innanje FedML 22 70 0 10 Sep 2022
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification Sajani Vithana S. Ulukus FedML 20 19 0 09 Sep 2022
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning Yan Kang Jiahuan Luo Yuanqin He Xiaojin Zhang Lixin Fan Qiang Yang FedML 11 15 0 08 Sep 2022
On the utility and protection of optimization with differential privacy and classic regularization techniques Eugenio Lomurno Matteo matteucci 38 9 0 07 Sep 2022
CPS Attack Detection under Limited Local Information in Cyber Security: A Multi-node Multi-class Classification Ensemble Approach Jun-Ying Liu Yifu Tang Haimeng Zhao X. Wang Fangyu Li Jingyi Zhang 34 5 0 01 Sep 2022
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces Shitong Sun Chenyang Si Guile Wu S. Gong FedML 30 0 0 29 Aug 2022
Flexible Vertical Federated Learning with Heterogeneous Parties Timothy Castiglia Shiqiang Wang S. Patterson FedML 42 34 0 26 Aug 2022
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation H. Roth Ali Hatamizadeh Ziyue Xu Can Zhao Wenqi Li Andriy Myronenko Daguang Xu FedML 37 9 0 22 Aug 2022
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering Rui Wang Xingkai Wang H. Chen Jérémie Decouchant S. Picek Ziqiang Liu K. Liang 38 1 0 22 Aug 2022
Practical Vertical Federated Learning with Unsupervised Representation Learning Zhaomin Wu Yue Liu Bingsheng He FedML 38 38 0 13 Aug 2022
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives Zichen Ma Yu Lu Wenye Li Shuguang Cui FedML 11 1 0 12 Aug 2022
Dropout is NOT All You Need to Prevent Gradient Leakage Daniel Scheliga Patrick Mäder M. Seeland FedML 42 12 0 12 Aug 2022
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee? A. Elkordy Jiang Zhang Yahya H. Ezzeldin Konstantinos Psounis A. Avestimehr FedML 35 38 0 03 Aug 2022
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning Hua Ma Qun Li Yifeng Zheng Zhi Zhang Xiaoning Liu Yan Gao S. Al-Sarawi Derek Abbott FedML 37 3 0 19 Jul 2022
Enhanced Security and Privacy via Fragmented Federated Learning N. Jebreel J. Domingo-Ferrer Alberto Blanco-Justicia David Sánchez FedML 39 26 0 13 Jul 2022
Hercules: Boosting the Performance of Privacy-preserving Federated Learning Guowen Xu Xingshuo Han Shengmin Xu Tianwei Zhang Hongwei Li Xinyi Huang R. Deng FedML 35 16 0 11 Jul 2022
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware H. Hashemi Yongqin Wang M. Annavaram FedML 26 58 0 30 Jun 2022
zPROBE: Zero Peek Robustness Checks for Federated Learning Zahra Ghodsi Mojan Javaheripi Nojan Sheybani Xinqiao Zhang Ke Huang F. Koushanfar FedML 50 18 0 24 Jun 2022
Privacy Preservation Among Honest-but-Curious Edge Nodes: A Survey Christian Badolato 12 1 0 22 Jun 2022
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application Youlong Ding Xueyang Wu Zhitao Li Zeheng Wu S. Tan Qian Xu Weike Pan Qiang Yang FedML 38 4 0 21 Jun 2022
Decentralized adaptive clustering of deep nets is beneficial for client collaboration Edvin Listo Zec Ebba Ekblom Martin Willbo Olof Mogren Sarunas Girdzijauskas OOD FedML 18 8 0 17 Jun 2022
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data Fangcheng Fu Huanran Xue Yong Cheng Yangyu Tao Bin Cui FedML 26 59 0 16 Jun 2022