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Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, 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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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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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 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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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Antti Tarvainen and Harri Valpola · 2017
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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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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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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
Earlier work this paper cites.
Sebastian U Stich and Sai Praneeth Karimireddy · 2019
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
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.
Robust federated learning in a heterogeneous environment
Avishek Ghosh, Justin Hong, Dong Yin, and Kannan Ramchandran · 2019
Cited alongside, same era.
A hybrid approach to privacy-preserving federated learning
Hierarchical federated learning across heterogeneous cellular networks
Mehdi Salehi Heydar Abad, Emre Ozfatura, Deniz Gunduz, and Ozgur Ercetin · 2020
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Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2020
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An efficient framework for clustered federated learning
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran · 2020
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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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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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Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
Cited alongside, same era.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Cited alongside, same era.
Towards utilizing unlabeled data in federated learning: A survey and prospective
Yilun Jin, Xiguang Wei, Yang Liu, and Qiang Yang · 2020
Cited alongside, same era.
Improving semi-supervised federated learning by reducing the gradient diversity of models
Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E Gonzalez, and Michael W Mahoney · 2020
Cited alongside, same era.
Federated semi-supervised learning with inter-client consistency & disjoint learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2020
Cited alongside, same era.
Later among the works it cites.
Semi-supervised federated learning for activity recognition
Yuchen Zhao, Hanyang Liu, Honglin Li, Payam Barnaghi, and Hamed Haddadi · 2020
Later among the works it cites.
Semi-supervised learning under class distribution mismatch
Yanbei Chen, Xiatian Zhu, Wei Li, and Shaogang Gong · 2020
Later among the works it cites.
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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A federated semi-supervised learning approach for network traffic classification
Yao Peng, Meirong He, and Yu Wang · 2021
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Semifl: Communication efficient semi-supervised federated learning with unlabeled clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
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