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We propose and analyze a new type of stochastic first order method: gradient descent with compressed iterates (GDCI).
Distributed learning with compressed gradient differences
Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč, and Peter Richtárik · 1901
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99% of distributed optimization is a waste of time: The issue and how to fix it
Konstantin Mishchenko, Filip Hanzely, and Peter Richtárik · 1901
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Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasia Koloskova, Sebastian U. Stich, and Martin Jaggi · 1902
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Decentralized Deep Learning with Arbitrary Communication Compression
Anastasia Koloskova, Tao Lin, Sebastian U. Stich, and Martin Jaggi · 1907
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Television by Pulse Code Modulation*
W. M. Goodall · 1951
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Picture coding using pseudo-random noise
L. Roberts · 1962
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Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Federated optimization: distributed machine learning for on-device intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2016
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QSGD: Communication-efficient SGD via Gradient Quantization and Encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Differentially Private Federated Learning: A Client Level Perspective
Robin C. Geyer, Tassilo Klein, and Moin Nabi · 2017
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Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 2017
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally · 2017
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Gradient Sparsification for Communication-Efficient Distributed Optimization
Jianqiao Wangni, Jialei Wang, Ji Liu, and Tong Zhang · 2017
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TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
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Decentralization Meets Quantization
Hanlin Tang, Ce Zhang, Shaoduo Gan, Tong Zhang, and Ji Liu · 2018
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When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan · 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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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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Tal Ben-Nun and Torsten Hoefler · 2018
Cited alongside, same era.
signSGD: Compressed Optimisation for Non-Convex Problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Anima Anandkumar · 2018
Cited alongside, same era.
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečný, H. Brendan McMahan, and Ameet Talwalkar · 2018
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A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
Peng Jiang and Gagan Agrawal · 2018
Cited alongside, same era.
Don’t Use Large Mini-Batches, Use Local SGD
Tao Lin, Sebastian U. Stich, and Martin Jaggi · 2018
Cited alongside, same era.
Local SGD Converges Fast and Communicates Little
Sebastian U. Stich · 2018
Cited alongside, same era.
Sebastian U. Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
Cited alongside, same era.
Jianyu Wang and Gauri Joshi
Cited in the paper.
Samuel Horvath, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, and Peter Richtárik · 2019
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Stochastic Distributed Learning with Gradient Quantization and Variance Reduction
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Sebastian Stich, and Peter Richtárik · 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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Federated Learning: Challenges, Methods, and Future Directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
T. Nishio and R. Yonetani · 2019
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Robust and Communication-Efficient Collaborative Learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, and Ramtin Pedarsani · 2019
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Federated Machine Learning: Concept and Applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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