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We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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A constant-factor approximation algorithm for the k-median problem
Moses Charikar, Sudipto Guha, Éva Tardos, and David B Shmoys · 2002
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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On coresets for k-means and k-median clustering
Sariel Har-Peled and Soham Mazumdar · 2004
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A constant factor approximation algorithm for k-median clustering with outliers
Ke Chen · 2008
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Keeping the resident in the loop: Adapting the smart home to the user
Parisa Rashidi and Diane J. Cook · 2009
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Stochastic convex optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
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Parallelized stochastic gradient descent
Martin Zinkevich, Markus Weimer, Lihong Li, and Alex J Smola · 2010
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A survey on wearable sensor-based systems for health monitoring and prognosis
Alexandros Pantelopoulos and Nikolaos G Bourbakis · 2010
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Clustering with spectral norm and the k-means algorithm
Amit Kumar and Ravindran Kannan · 2010
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu · 2011
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Communication efficient distributed optimization using an approximate newton-type method
Ohad Shamir, Nathan Srebro, and Tong Zhang · 2013
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Jiashi Feng, Huan Xu, and Shie Mannor · 2014
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Alternating minimization for mixed linear regression
Xinyang Yi, Constantine Caramanis, and Sujay Sanghavi · 2014
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Geometric median and robust estimation in banach spaces
Stanislav Minsker · 2015
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Federated optimization: distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Cocoa: A general framework for communication-efficient distributed optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takác, Michael I. Jordan, and Martin Jaggi · 2016
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
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Defending against saddle point attack in Byzantine-robust distributed learning
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett · 2018
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Gradient diversity: a key ingredient for scalable distributed learning
Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, and Peter Bartlett · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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Kevin Lai, Anup Rao, and S Vempala · 2016
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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
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Statistical and computational guarantees of lloyd’s algorithm and its variants
Yu Lu and Harrison H Zhou · 2016
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Federated learning: Collaborative machine learning without centralized training data
Brendan McMahan and Daniel Ramage · 2017
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Byzantine-tolerant machine learning
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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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Local search methods for k-means with outliers
Shalmoli Gupta, Ravi Kumar, Kefu Lu, Benjamin Moseley, and Sergei Vassilvitskii · 2017
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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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Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, and Qing Ling · 2018
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Byzantine stochastic gradient descent
Dan Alistarh, Zeyuan Allen-Zhu, and Jerry Li · 2018
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Generalized Byzantine-tolerant SGD
Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta · 2018
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Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling · 2018
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Constant approximation for k-median and k-means with outliers via iterative rounding
Ravishankar Krishnaswamy, Shi Li, and Sai Sandeep · 2018
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Learning mixtures of sparse linear regressions using sparse graph codes
Dong Yin, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran · 2018
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Being robust (in high dimensions) can be practical
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Stewart Alistair · 2018
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Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Closest in time.
Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
Closest in time.