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To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods.
The byzantine generals problem
Leslie Lamport, Robert Shostak, and Marshall Pease · 1982
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Parallel distributed computing using python
Lisandro D Dalcin, Rodrigo R Paz, Pablo A Kler, and Alejandro Cosimo · 2011
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Chernoff’s inequality-a very elementary proof
Nathan Linial and Zur Luria · 2014
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Geometric median and robust estimation in banach spaces
Stanislav Minsker et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Hoeffding’s inequality for sums of weakly dependent random variables
J. Ramon C. Pelekis · 2017
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Draco: Byzantine-resilient distributed training via redundant gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2018
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Data encoding for byzantine-resilient distributed gradient descent
Deepesh Data, Linqi Song, and Suhas Diggavi · 2018
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Lagrange coded computing: Optimal design for resiliency, security and privacy
Byzantine stochastic gradient descent
Dan Alistarh, Zeyuan Allen-Zhu, and Jerry Li · 2018
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signsgd with majority vote is communication efficient and fault tolerant
Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, and Anima Anandkumar · 2018
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A little is enough: Circumventing defenses for distributed learning
Moran Baruch, Gilad Baruch, and Yoav Goldberg · 2019
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Sgd: Decentralized byzantine resilience
El-Mahdi El-Mhamdi, Rachid Guerraoui, Arsany Guirguis, and Sebastien Rouault · 2019
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Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 2019
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Qian Yu, Netanel Raviv, Jinhyun So, and A Salman Avestimehr · 2018
Cited alongside, same era.
The hidden vulnerability of distributed learning in byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault · 2018
Cited alongside, same era.
Generalized byzantine-tolerant sgd
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta
Cited in the paper.
Aggregathor: Byzantine machine learning via robust gradient aggregation
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, and Sebastien Guirguis, Arsany Rouault
Cited in the paper.
Defending against saddle point attack in byzantine-robust distributed learning
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett
Cited in the paper.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett
Cited in the paper.
Zeno: Byzantine-suspicious stochastic gradient descent
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta
Cited in the paper.
Fast and secure distributed learning in high dimension
El-Mahdi El-Mhamdi and Rachid Guerraoui · 2019
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Sub-gaussian estimators of the mean of a random vector
Gábor Lugosi, Shahar Mendelson, et al · 2019
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