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Federated Learning allows the training of machine learning models by using the computation and private data resources of many distributed clients.
On estimating regression
Elizbar A Nadaraya · 1964
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Smooth regression analysis
Geoffrey S Watson · 1964
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Probability of error of some adaptive pattern-recognition machines
Henry Scudder · 1965
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Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis
Geoffrey J McLachlan · 1975
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A distribution-free theory of nonparametric regression
László Györfi, Michael Kohler, Adam Krzyżak, and Harro Walk · 2002
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Tri-training: Exploiting unlabeled data using three classifiers
Zhi-Hua Zhou and Ming Li · 2005
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Semi-supervised learning by entropy minimization
Yves Grandvalet, Yoshua Bengio, et al · 2005
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Fast learning rates for plug-in classifiers under the margin condition
Jean-Yves Audibert and Alexandre B Tsybakov · 2005
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On the rate of convergence of local averaging plug-in classification rules under a margin condition
Michael Kohler and Adam Krzyzak · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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A probabilistic theory of pattern recognition
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
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Learning with pseudo-ensembles
Philip Bachman, Ouais Alsharif, and Doina Precup · 2014
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Semi-supervised learning with ladder networks
Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko · 2015
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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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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Self-ensembling for visual domain adaptation
Geoffrey French, Michal Mackiewicz, and Mark Fisher · 2017
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Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Ruslan Salakhutdinov · 2017
The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 2020
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Towards utilizing unlabeled data in federated learning: A survey and prospective
Yilun Jin, Xiguang Wei, Yang Liu, and Qiang Yang · 2020
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Semi-supervised federated learning for activity recognition
Yuchen Zhao, Hanyang Liu, Honglin Li, Payam Barnaghi, and Hamed Haddadi · 2020
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Fedsemi: An adaptive federated semi-supervised learning framework
Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, and Fenglong Ma · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Antti Tarvainen and Harri Valpola · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Slowmo: Improving communication-efficient distributed sgd with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael Rabbat · 2019
Cited alongside, same era.
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
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Fedbn: Federated learning on non-iid features via local batch normalization
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou · 2021
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HeteroFL: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
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Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2021
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Federated semi-supervised learning with inter-client consistency & disjoint learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2021
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Improving semi-supervised federated learning by reducing the gradient diversity of models
Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E. Gonzalez, Kannan Ramchandran, and Michael W. Mahoney · 2021
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Federated semi-supervised learning for covid region segmentation in chest ct using multi-national data from china, italy, japan
Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, et al · 2021
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama · 2021
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Fedmix: Approximation of mixup under mean augmented federated learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang · 2021
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