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In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round.
Learning multiple layers of features from tiny images
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Scaling distributed machine learning with the parameter server
Mu Li, David G Andersen, Jun Woo Park, Alexander J Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J Shekita, and Bor-Yiing Su · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
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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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Communication-efficient learning of deep networks from decentralized data
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Network topology and communication-computation tradeoffs in decentralized optimization
Angelia Nedić, Alex Olshevsky, and Michael G Rabbat · 2018
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Jianyu Wang and Gauri Joshi · 2018
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On the convergence properties of a k-step averaging stochastic gradient descent algorithm for nonconvex optimization
Fan Zhou and Guojing Cong · 2018
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Adaptive communication strategies to achieve the best error-runtime trade-off in local-update SGD
Jianyu Wang and Gauri Joshi · 2018
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, and Nati Srebro · 2018
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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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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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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Local SGD converges fast and communicates little
Sebastian U Stich · 2019
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Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 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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Trading redundancy for communication: Speeding up distributed SGD for non-convex optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck Cadambe · 2019
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Local SGD with periodic averaging: Tighter analysis and adaptive synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck Cadambe · 2019
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Sebastian U Stich and Sai Praneeth Karimireddy · 2019
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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
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Variance reduced local SGD with lower communication complexity
Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, and Yifei Cheng · 2019
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On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
Hao Yu, Rong Jin, and Sen Yang · 2019
Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 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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Tighter theory for local SGD on identical and heterogeneous data
A Khaled, K Mishchenko, and P Richtárik · 2020
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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 · 2020
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Is local SGD better than minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U Stich, Zhen Dai, Brian Bullins, H Brendan McMahan, Ohad Shamir, and Nathan Srebro · 2020
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A unified theory of decentralized SGD with changing topology and local updates
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A distributed hierarchical SGD algorithm with sparse global reduction
Fan Zhou and Guojing Cong · 2019
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First analysis of local GD on heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2019
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Local AdaAlter: Communication-efficient stochastic gradient descent with adaptive learning rates
Cong Xie, Oluwasanmi Koyejo, Indranil Gupta, and Haibin Lin · 2019
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Communication trade-offs for local-SGD with large step size
Aymeric Dieuleveut and Kumar Kshitij Patel · 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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Asynchronous federated optimization
Cong Xie, Sanmi Koyejo, and Indranil Gupta · 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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Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian U Stich · 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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Faster on-device training using new federated momentum algorithm
Zhouyuan Huo, Qian Yang, Bin Gu, Lawrence Carin Huang, et al · 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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Don’t use large mini-batches, use local SGD
Tao Lin, Sebastian U Stich, and Martin Jaggi · 2020
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From local SGD to local fixed point methods for federated learning
Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtarik · 2020
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Overlap local-SGD: An algorithmic approach to hide communication delays in distributed SGD
Jianyu Wang, Hao Liang, and Gauri Joshi · 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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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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Acceleration for compressed gradient descent in distributed and federated optimization
Zhize Li, Dmitry Kovalev, Xun Qian, and Peter Richtárik · 2020
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On the convergence of quantized parallel restarted sgd for serverless learning
Feijie Wu, Shiqi He, Yutong Yang, Haozhao Wang, Zhihao Qu, and Song Guo · 2020
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Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
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