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Federated Learning in Adversarial Settings

Federated Learning in Adversarial Settings

15 October 2020

Raouf Kerkouche

C. Castelluccia

Papers citing "Federated Learning in Adversarial Settings"

10 / 10 papers shown

Title
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 54 176 0 07 Oct 2019
Rényi Differential Privacy of the Sampled Gaussian Mechanism Ilya Mironov Kunal Talwar Li Zhang 78 283 0 28 Aug 2019
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 152 1,474 0 10 May 2018
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Chengyue Wu Song Han Huizi Mao Yu Wang W. Dally 133 1,407 0 05 Dec 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,883 0 25 Aug 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning W. Wen Cong Xu Feng Yan Chunpeng Wu Yandan Wang Yiran Chen Hai Helen Li 140 989 0 22 May 2017
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 306 4,646 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 207 6,130 0 01 Jul 2016
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,672 0 21 Dec 2014
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response Ulfar Erlingsson Vasyl Pihur Aleksandra Korolova 96 1,992 0 25 Jul 2014

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