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Local Model Reconstruction Attacks in Federated Learning and their Uses

28 October 2022

Papers citing "Local Model Reconstruction Attacks in Federated Learning and their Uses"

33 / 33 papers shown

Title
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 103 123 0 31 Mar 2022
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning Yangsibo Huang Samyak Gupta Zhao Song Kai Li Sanjeev Arora FedML AAML SILM 77 277 0 30 Nov 2021
Source Inference Attacks in Federated Learning Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Xuyun Zhang 55 82 0 13 Sep 2021
FedMix: Approximation of Mixup under Mean Augmented Federated Learning Tehrim Yoon Sumin Shin Sung Ju Hwang Eunho Yang FedML 76 172 0 01 Jul 2021
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp Qi Dou OOD FedML 278 824 0 15 Feb 2021
secureTF: A Secure TensorFlow Framework D. Quoc Franz Gregor Sergei Arnautov Roland Kunkel Pramod Bhatotia Christof Fetzer 90 40 0 20 Jan 2021
Optimal Client Sampling for Federated Learning Jiajun He Samuel Horváth Peter Richtárik FedML 89 201 0 26 Oct 2020
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies Yae Jee Cho Jianyu Wang Gauri Joshi FedML 157 408 0 03 Oct 2020
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning S. Kadhe Nived Rajaraman O. O. Koyluoglu Kannan Ramchandran FedML 90 164 0 23 Sep 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 121 1,234 0 31 Mar 2020
iDLG: Improved Deep Leakage from Gradients Bo Zhao Konda Reddy Mopuri Hakan Bilen FedML 81 643 0 08 Jan 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 275 6,294 0 10 Dec 2019
Model Fusion via Optimal Transport Sidak Pal Singh Martin Jaggi MoMe FedML 120 241 0 12 Oct 2019
Differential Privacy-enabled Federated Learning for Sensitive Health Data Olivia Choudhury A. Gkoulalas-Divanis Theodoros Salonidis I. Sylla Yoonyoung Park Grace Hsu Amar K. Das FedML OOD 72 176 0 07 Oct 2019
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang Kun Xu Jun Zhu AAML 83 105 0 25 Sep 2019
Tighter Theory for Local SGD on Identical and Heterogeneous Data Ahmed Khaled Konstantin Mishchenko Peter Richtárik 84 435 0 10 Sep 2019
Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate Stefan Vlaski Ali H. Sayed 83 68 0 03 Jul 2019
Interpret Federated Learning with Shapley Values Guan Wang FAtt FedML 66 92 0 11 May 2019
Differentially Private Model Publishing for Deep Learning Lei Yu Ling Liu C. Pu Mehmet Emre Gursoy Stacey Truex FedML 85 265 0 03 Apr 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 158 1,423 0 03 Dec 2018
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization Dongruo Zhou Yiqi Tang Yuan Cao Ziyan Yang Quanquan Gu 79 150 0 16 Aug 2018
Subsampled Rényi Differential Privacy and Analytical Moments Accountant Yu Wang Borja Balle S. Kasiviswanathan 88 401 0 31 Jul 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 87 393 0 04 Jul 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 159 1,482 0 10 May 2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates Dong Yin Yudong Chen Kannan Ramchandran Peter L. Bartlett OOD FedML 131 1,517 0 05 Mar 2018
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 316 9,811 0 25 Oct 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Ce Zhang Huan Zhang Cho-Jui Hsieh Wei Zhang Ji Liu 62 1,235 0 25 May 2017
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables Chris J. Maddison A. Mnih Yee Whye Teh BDL 200 2,541 0 02 Nov 2016
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 280 4,160 0 18 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 109 1,811 0 09 Sep 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,172 0 01 Jul 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 467 43,347 0 11 Feb 2015