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We study Byzantine collaborative learning, where $n$ nodes seek to collectively learn from each others' local data.
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Scaling distributed machine learning with the parameter server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Bor-Yiing Su · 2014
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Peva Blanchard, El-Mahdi El-Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Dan Alistarh, Zeyuan Allen-Zhu, and Jerry Li · 2018
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Sourcing and automation of political news and information during three european elections
Lisa-Maria Neudert, Philip Howard, and Bence Kollanyi · 2019
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Pytorch: An imperative style, high-performance deep learning library
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Shashank Rajput, Hongyi Wang, Zachary Charles, and Dimitris Papailiopoulos · 2019
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Bridge: Byzantine-resilient decentralized gradient descent
Zhixiong Yang and Waheed U Bajwa · 2019
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Byrdie: Byzantine-resilient distributed coordinate descent for decentralized learning
Zhixiong Yang and Waheed U Bajwa · 2019
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