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We address the problem of federated learning (FL) where users are distributed and partitioned into clusters.
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 1910
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
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Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
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Low-rank matrix completion using alternating minimization
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi · 2013
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Phase retrieval using alternating minimization
Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi · 2013
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Mark Rudelson and Roman Vershynin · 2013
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Scaling distributed machine learning with the parameter server
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Learning mixtures of linear classifiers
Yuekai Sun, Stratis Ioannidis, and Andrea Montanari · 2014
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Alternating minimization for mixed linear regression
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2014
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Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling · 2018
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On the convergence of federated optimization in heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
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Communication-efficient learning of deep networks from decentralized data
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Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2016
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Overcoming catastrophic forgetting in neural networks
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Learning mixtures of sparse linear regressions using sparse graph codes
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Federated learning with non-iid data
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Agnostic federated learning
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
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