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Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing.
The power of two choices in randomized load balancing
M. Mitzenmacher · 1996
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Large scale distributed deep networks
Jeffrey Dean, Greg S. Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc’Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng · 2012
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agøura 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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Parallel restarted SGD for non-convex optimization with faster convergence and less communication
Hao Yu, Sen Yang, and Shenghuo Zhu · 2018
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Local SGD converges fast and communicates little
Sebastian U Stich · 2018
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Jianyu Wang and Gauri Joshi · 2018
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Not all samples are created equal: Deep learning with importance sampling
A. Katharopoulos and F. Fleuret · 2018
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Coordinate descent with bandit sampling, 2018
Farnood Salehi, Patrick Thiran, and Elisa Celis · 2018
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Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurelien 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, Adria Gascon, 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 Konecny, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Ozgur, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramer, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
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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 Konecny, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
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Adaptive Communication Strategies for Best Error-Runtime Trade-offs in Communication-Efficient Distributed SGD
Jianyu Wang and Gauri Joshi · 2019
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On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi · 2019
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Sebastian U Stich and Sai Praneeth Karimireddy · 2019
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Federated optimization for heterogeneous networks
Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 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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Active federated learning
Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu, and Anuj Kumar · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
A unified theory of decentralized SGD with changing topology and local updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian U Stich · 2020
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Faster on-device training using new federated momentum algorithm
Zhouyuan Huo, Qian Yang, Bin Gu, Lawrence Carin, and Heng Huang · 2020
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FedPD: A federated learning framework with optimal rates and adaptivity to non-IID data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 2020
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FedSplit: An algorithmic framework for fast federated optimization
Reese Pathak and Martin J Wainwright · 2020
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From local SGD to local fixed point methods for federated learning
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Cited alongside, same era.
Accelerating deep learning by focusing on the biggest losers, 2019
Angela H. Jiang, Daniel L. K. Wong, Giulio Zhou, David G. Andersen, Jeffrey Dean, Gregory R. Ganger, Gauri Joshi, Michael Kaminksy, Michael Kozuch, Zachary C. Lipton, and Padmanabhan Pillai · 2019
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, and Virginia Smith · 2019
Cited alongside, same era.
Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Robust aggregation for federated learning
Krishna Pillutla, Sham M. Kakade, and Zaid Harchaoui · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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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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Tighter theory for local SGD on identical and heterogeneous data
A Khaled, K Mishchenko, and P Richtárik · 2020
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Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtarik · 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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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
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Towards flexible device participation in federated learning for non-iid data
Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, and Carlee Joe-Wong · 2020
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Device heterogeneity in federated learning: A superquantile approach
Yassine Laguel, Krishna Pillutla, Jérôme Malick, and Zaid Harchaoui · 2020
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Communication-efficient federated learning via optimal client sampling
Mónica Ribero and Haris Vikalo · 2020
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Choosing the sample with lowest loss makes sgd robust
Vatsal Shah, Xiaoxia Wu, and Sujay Sanghavi · 2020
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A fairness-aware incentive scheme for federated learning
Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, Dusit Niyato, and Qiang Yang · 2020
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Towards Fair and Privacy-Preserving Federated Deep Models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, and Kee Siong Ng · 2020
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