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Federated Learning (FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by a central server.
Robust federated learning in a heterogeneous environment
Avishek Ghosh, Justin Hong, Dong Yin, and Kannan Ramchandran · 1906
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
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The Complete Works of William Shakespeare
W. Shakespeare · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
Cited alongside, same era.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Cited alongside, same era.
Byzantine stochastic gradient descent
Dan Alistarh, Zeyuan Allen-Zhu, and Jerry Li · 2018
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
Cited alongside, same era.
Federated learning with non-iid data, 2018
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H Brendan McMahan, et al · 2019
Later among the works it cites.
Leaf: A benchmark for federated settings, 2019
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2019
Later among the works it cites.
On the convergence of local descent methods in federated learning, 2019
Farzin Haddadpour and Mehrdad Mahdavi · 2019
Later among the works it cites.
Advances and open problems in federated learning, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
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Cited alongside, same era.
Distributed training with heterogeneous data: Bridging median- and mean-based algorithms, 2019a
Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, and Mingyi Hong
Cited in the paper.
Communication-efficient federated deep learning with asynchronous model update and temporally weighted aggregation, 2019b
Yang Chen, Xiaoyan Sun, and Yaochu Jin
Cited in the paper.
Rsa: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets
Liping Li, Wei Xu, Tianyi Chen, Georgios B Giannakis, and Qing Ling
Cited in the paper.
Later among the works it cites.
Robust aggregation for federated learning, 2019
Krishna Pillutla, Sham M. Kakade, and Zaid Harchaoui · 2019
Later among the works it cites.
Adaptive federated optimization, 2020
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan · 2020
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