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Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL).
The mnist database of handwritten digits
Yann LeCun · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Communication-efficient algorithms for statistical optimization
Yuchen Zhang, John C Duchi, and Martin J Wainwright · 2013
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally · 2015
Earlier work this paper cites.
Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
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Stochastic optimization with importance sampling for regularized loss minimization
Peilin Zhao and Tong Zhang · 2015
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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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Fan Zhou and Guojing Cong · 2017
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Lag: Lazily aggregated gradient for communication-efficient distributed learning
Tianyi Chen, Georgios Giannakis, Tao Sun, and Wotao Yin · 2018
Earlier work this paper cites.
Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
Cited alongside, same era.
Local sgd converges fast and communicates little
Sebastian U Stich · 2018
Cited alongside, same era.
Sparsified sgd with memory
Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
Cited alongside, same era.
Local sgd with periodic averaging: Tighter analysis and adaptive synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck Cadambe · 2019
Cited alongside, same era.
On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi · 2019
Cited alongside, same era.
On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Budgeted online selection of candidate iot clients to participate in federated learning
Ihab Mohammed, Shadha Tabatabai, Ala Al-Fuqaha, Faissal El Bouanani, Junaid Qadir, Basheer Qolomany, and Mohsen Guizani · 2020
Later among the works it cites.
Fedfast: Going beyond average for faster training of federated recommender systems
Khalil Muhammad, Qinqin Wang, Diarmuid O’Reilly-Morgan, Elias Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor · 2020
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Federated expectation maximization with heterogeneity mitigation and variance reduction
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, and Geneviève Robin · 2021
Later among the works it cites.
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Cited alongside, same era.
Sparq-sgd: Event-triggered and compressed communication in decentralized stochastic optimization
Navjot Singh, Deepesh Data, Jemin George, and Suhas Diggavi · 2019
Cited alongside, same era.
Optimal client sampling for federated learning
Wenlin Chen, Samuel Horvath, and Peter Richtarik · 2020
Cited alongside, same era.
Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
Cited alongside, same era.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Clustered sampling: Low-variance and improved representativity for clients selection in federated learning
Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 2021
Later among the works it cites.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Later among the works it cites.
Optimal importance sampling for federated learning
Elsa Rizk, Stefan Vlaski, and Ali H Sayed · 2021
Later among the works it cites.
Cooperative sgd: A unified framework for the design and analysis of local-update sgd algorithms
Jianyu Wang and Gauri Joshi · 2021
Later among the works it cites.
Diverse client selection for federated learning via submodular maximization
Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, and Jeff Bilmes · 2022
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