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Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models.
“Federated optimization: Distributed machine learning for on-device intelligence,”
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik, · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition,”
K. He, X. Zhang, S. Ren, and J. Sun, · 2016
Earlier work this paper cites.
“Communication-Efficient Learning of Deep Networks from Decentralized Data,”
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agøura y Arcas, · 2017
Earlier work this paper cites.
“Qsgd: Communication-efficient sgd via gradient quantization and encoding,”
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic, · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Distributed mean estimation with limited communication,”
Ananda Theertha Suresh, X Yu Felix, Sanjiv Kumar, and H Brendan McMahan, · 2017
Earlier work this paper cites.
“Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,”
Han Xiao, Kashif Rasul, and Roland Vollgraf, · 2017
Earlier work this paper cites.
“Cooperative SGD: Unifying Temporal and Spatial Strategies for Communication-Efficient Distributed SGD,”
Jianyu Wang and Gauri Joshi, · 2018
Earlier work this paper cites.
“Local sgd converges fast and communicates little,”
Sebastian U Stich, · 2018
Cited alongside, same era.
“Atomo: Communication-efficient learning via atomic sparsification,”
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, and Stephen Wright, · 2018
Cited alongside, same era.
“Optimization methods for large-scale machine learning,”
Léon Bottou, Frank E Curtis, and Jorge Nocedal, · 2018
Cited alongside, same era.
“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
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
“Communication-efficient distributed learning via lazily aggregated quantized gradients,”
Jun Sun, Tianyi Chen, Georgios B Giannakis, and Zaiyue Yang, · 2019
Later among the works it cites.
“Adaptive Communication Strategies for Best Error-Runtime Trade-offs in Communication-Efficient Distributed SGD,”
Jianyu Wang and Gauri Joshi, · 2019
Later among the works it cites.
“Pytorch: An imperative style, high-performance deep learning library,”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala, · 2019
Later among the works it cites.
“Overlap local-SGD: An algorithmic approach to hide communication delays in distributed SGD,”
Jianyu Wang, Hao Liang, and Gauri Joshi, · 2020
Later among the works it cites.
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Cited alongside, same era.
“vqsgd: Vector quantized stochastic gradient descent,” 2019
Venkata Gandikota, Daniel Kane, Raj Kumar Maity, and Arya Mazumdar, · 2019
Cited alongside, same era.
“Powersgd: Practical low-rank gradient compression for distributed optimization,”
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi, · 2019
Cited alongside, same era.
“Cifar-10 (canadian institute for advanced research),”
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton,
Cited in the paper.
Nir Shlezinger, Mingzhe Chen, Yonina C Eldar, H Vincent Poor, and Shuguang Cui, · 2020
Later among the works it cites.
“Adaptive gradient quantization for data-parallel sgd,”
Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel Roy, and Ali Ramezani-Kebrya, · 2020
Later among the works it cites.
“Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization,”
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani, · 2031
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