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While client sampling is a central operation of current state-of-the-art federated learning (FL) approaches, the impact of this procedure on the convergence and speed of FL remains under-investigated.
Reservoir-sampling algorithms of time complexity o(n(1 + log(n/n)))
Kim-Hung Li · 1994
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Alex Krizhevsky · 2009
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Importance sampling for minibatches
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Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Jianyu Wang and Gauri Joshi · 2018
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Atomo: Communication-efficient learning via atomic sparsification
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, and Stephen Wright · 2018
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Qsparse-local-sgd: Distributed sgd with quantization, sparsification and local computations
Debraj Basu, Deepesh Data, Can Karakus, and Suhas Diggavi · 2019
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On the convergence of local descent methods in federated learning, 2019
Farzin Haddadpour and Mehrdad Mahdavi · 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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On the convergence of stochastic gradient descent with adaptive stepsizes
Xiaoyu Li and Francesco Orabona · 2019
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Feddane: A federated newton-type method
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smithy · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
Takayuki Nishio and Ryo Yonetani · 2019
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Efficient algorithms for modifying and sampling from a categorical distribution
Daniel Tang · 2019
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Matcha: Speeding up decentralized sgd via matching decomposition sampling
Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang, Gauri Joshi, and Soummya Kar · 2019
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Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtarik · 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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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 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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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
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Dynamic federated learning
Elsa Rizk, Stefan Vlaski, and Ali H. Sayed · 2020
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Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
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AdaGrad stepsizes: Sharp convergence over nonconvex landscapes
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Client selection in federated learning: Convergence analysis and power-of-choice selection strategies, 2020
Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
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Optimal user selection for high-performance and stabilized energy-efficient federated learning platforms
Joohyung Jeon, Soohyun Park, Minseok Choi, Joongheon Kim, Young-Bin Kwon, and Sungrae Cho · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
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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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Slowmo: Improving communication-efficient distributed sgd with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael Rabbat · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama · 2021
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Clustered sampling: Low-variance and improved representativity for clients selection in federated learning
Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi · 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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