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Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective.
Some methods of speeding up the convergence of iteration methods
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Boris T Polyak and Anatoli B Juditsky · 1992
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Metropolized independent sampling with comparisons to rejection sampling and importance sampling
Jun S Liu · 1996
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An evaluation of statistical approaches to text categorization
Yiming Yang · 1999
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A re-examination of text categorization methods
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Is local sgd better than minibatch sgd?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U Stich, Zhen Dai, Brian Bullins, H Brendan McMahan, Ohad Shamir, and Nathan Srebro · 2002
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Design of experiments of the nips 2003 variable selection benchmark
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A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics
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Minibatch vs local sgd for heterogeneous distributed learning
Blake Woodworth, Kumar Kshitij Patel, and Nathan Srebro · 2006
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael I Jordan · 2008
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Learning multiple layers of features from tiny images
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Shrinkage algorithms for mmse covariance estimation
Yilun Chen, Ami Wiesel, Yonina C Eldar, and Alfred O Hero · 2010
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Machine learning: a probabilistic perspective
Kevin P Murphy · 2012
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BIG & QUIC: Sparse inverse covariance estimation for a million variables
Cho-Jui Hsieh, Mátyás A Sustik, Inderjit S Dhillon, Pradeep K Ravikumar, and Russell Poldrack · 2013
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Asymptotically exact, embarrassingly parallel mcmc
Willie Neiswanger, Chong Wang, and Eric Xing · 2013
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Monte Carlo theory, methods and examples
Art B. Owen · 2013
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Matthew D Hoffman and Andrew Gelman · 2014
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A tutorial on multilabel learning
Eva Gibaja and Sebastián Ventura · 2015
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A complete recipe for stochastic gradient mcmc
Yi-An Ma, Tianqi Chen, and Emily Fox · 2015
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
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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 Konečnỳ, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
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The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B Gibbons · 2019
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Introducing tensorflow federated, 2019
Alex Ingerman and Krzys Ostrowski · 2019
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Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
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Bayes and big data: The consensus monte carlo algorithm
Steven L Scott, Alexander W Blocker, Fernando V Bonassi, Hugh A Chipman, Edward I George, and Robert E McCulloch · 2016
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StackOverflow · 2016
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Emnist: Extending mnist to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Stochastic gradient descent as approximate bayesian inference
Stephan Mandt, Matthew D Hoffman, and David M Blei · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Scaffold: Stochastic controlled averaging for on-device federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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Analysis of sgd with biased gradient estimators
Ahmad Ajalloeian and Sebastian U Stich · 2020
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On the outsized importance of learning rates in local update methods
Zachary Charles and Jakub Konečnỳ · 2020
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Mime: Mimicking centralized stochastic algorithms in federated learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2020
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FedSplit: an algorithmic framework for fast federated optimization
Reese Pathak and Martin J Wainwright · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
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Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
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