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In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers.
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 1912
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Simplified neuron model as a principal component analyzer
Erkki Oja · 1982
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Fast convergence of stochastic gradient descent under a strong growth condition
Mark Schmidt and Nicolas Le Roux · 2013
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Mlitb: machine learning in the browser
Edward Meeds, Remco Hendriks, Said Al Faraby, Magiel Bruntink, and Max Welling · 2015
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Geometric median and robust estimation in banach spaces
Stanislav Minsker et al · 2015
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Ken Miura and Tatsuya Harada · 2015
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Geometric median in nearly linear time
Michael B Cohen, Yin Tat Lee, Gary Miller, Jakub Pachocki, and Aaron Sidford · 2016
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Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 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
Yudong Chen, Lili Su, and Jiaming Xu · 2017
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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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Analyzing federated learning through an adversarial lens, 2018
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2018
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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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The hidden vulnerability of distributed learning in byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault · 2018
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The power of interpolation: Understanding the effectiveness of sgd in modern over-parametrized learning
Siyuan Ma, Raef Bassily, and Mikhail Belkin · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 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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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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Aggregathor: Byzantine machine learning via robust gradient aggregation
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Hany Abdelmessih Guirguis, and Sébastien Louis Alexandre Rouault · 2019
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Robust federated learning in a heterogeneous environment
Avishek Ghosh, Justin Hong, Dong Yin, and Kannan Ramchandran · 2019
Byzantine-resilient sgd in high dimensions on heterogeneous data
Deepesh Data and Suhas Diggavi · 2020
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Resilience in collaborative optimization: redundant and independent cost functions
Nirupam Gupta and Nitin H Vaidya · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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Fast and furious convergence: Stochastic second order methods under interpolation
Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, and Simon Lacoste-Julien · 2020
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Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U Stich, and Martin Jaggi · 2019
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The byzantine generals problem
Leslie Lamport, Robert Shostak, and Marshall Pease · 2019
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RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets
Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Robust Aggregation for Federated Learning
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Detox: A redundancy-based framework for faster and more robust gradient aggregation
Shashank Rajput, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2019
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Can you really backdoor federated learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan · 2019
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On the byzantine robustness of clustered federated learning
F. Sattler, K. Müller, T. Wiegand, and W. Samek · 2020
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Attack of the tails: Yes, you really can backdoor federated learning
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Federated variance-reduced stochastic gradient descent with robustness to byzantine attacks
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Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
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Byzantine-resilient non-convex stochastic gradient descent
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