Decentralized gradient tracking with local steps-Reference-Cited by-同舟云学术

Decentralized gradient tracking with local steps

Published:2024-03-14 Issue: Volume: Page:1-28
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Liu Yue¹,Lin Tao²,Koloskova Anastasia³,Stich Sebastian U.⁴

Affiliation:

1. University of Toronto, Toronto, Canada

2. Westlake University, Zhejiang, People's Republic of China

3. EPFL, Ecublens, Switzerland

4. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2024.2322095

Reference33 articles.

1. Linear convergence of primal–dual gradient methods and their performance in distributed optimization

2. Chen et al. Accelerating gossip SGD with periodic global averaging in ICML 2021.

3. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

4. P. Goyal P. Dollár R.B. Girshick P. Noordhuis L. Wesolowski A. Kyrola A. Tulloch Y. Jia and K. He Accurate large minibatch SGD: Training ImageNet in 1 hour arXiv preprint arXiv:1706.02677 2017.

5. F. Haddadpour M.M. Kamani M. Mahdavi and V. Cadambe Local SGD with periodic averaging: Tighter analysis and adaptive synchronization in NeurIPS 2019.

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