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2105.08023
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Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD
Journal of machine learning research (JMLR), 2021
17 May 2021
Kun Yuan
Sulaiman A. Alghunaim
Xinmeng Huang
Re-assign community
ArXiv (abs)
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Papers citing
"Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD"
24 / 24 papers shown
Row-stochastic matrices can provably outperform doubly stochastic matrices in decentralized learning
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183
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Generalization Error Analysis for Attack-Free and Byzantine-Resilient Decentralized Learning with Data Heterogeneity
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Tao Sun
Qing Ling
FedML
281
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11 Jun 2025
On the Trade-off between Flatness and Optimization in Distributed Learning
Ying Cao
Zhaoxian Wu
Kun Yuan
Ali H. Sayed
579
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28 Jun 2024
Adjacent Leader Decentralized Stochastic Gradient Descent
European Conference on Artificial Intelligence (ECAI), 2024
Haoze He
Jing Wang
A. Choromańska
241
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0
18 May 2024
An Accelerated Distributed Stochastic Gradient Method with Momentum
Kun-Yen Huang
Shi Pu
Angelia Nedić
420
15
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15 Feb 2024
Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
Boao Kong
Shuchen Zhu
Songtao Lu
Xinmeng Huang
Kun Yuan
476
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0
05 Feb 2024
Stochastic Controlled Averaging for Federated Learning with Communication Compression
International Conference on Learning Representations (ICLR), 2023
Xinmeng Huang
Ping Li
Xiaoyun Li
453
279
0
16 Aug 2023
Momentum Benefits Non-IID Federated Learning Simply and Provably
International Conference on Learning Representations (ICLR), 2023
Ziheng Cheng
Xinmeng Huang
Pengfei Wu
Kun Yuan
FedML
706
38
0
28 Jun 2023
Distributed Random Reshuffling Methods with Improved Convergence
IEEE Transactions on Automatic Control (TAC), 2023
Kun-Yen Huang
Linli Zhou
Shi Pu
403
5
0
21 Jun 2023
DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm
International Conference on Machine Learning (ICML), 2023
Lisang Ding
Kexin Jin
Bicheng Ying
Kun Yuan
W. Yin
280
12
0
01 Jun 2023
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Neural Information Processing Systems (NeurIPS), 2023
Yutong He
Xinmeng Huang
Kun Yuan
376
21
0
25 May 2023
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
IEEE Transactions on Automatic Control (TAC), 2023
Kun-Yen Huang
Shin-Yi Pu
350
20
0
14 Jan 2023
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks
Xinmeng Huang
Kun Yuan
235
9
0
01 Nov 2022
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Neural Information Processing Systems (NeurIPS), 2022
Kun Yuan
Xinmeng Huang
Yiming Chen
Xiaohan Zhang
Yingya Zhang
Pan Pan
292
33
0
14 Oct 2022
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Neural Information Processing Systems (NeurIPS), 2022
Xinmeng Huang
Yiming Chen
W. Yin
Kun Yuan
422
41
0
08 Jun 2022
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Neural Information Processing Systems (NeurIPS), 2022
Xinmeng Huang
Dong-hwan Lee
Guang Cheng
Hamed Hassani
371
3
0
01 Jun 2022
Data-heterogeneity-aware Mixing for Decentralized Learning
Yatin Dandi
Anastasia Koloskova
Martin Jaggi
Sebastian U. Stich
278
22
0
13 Apr 2022
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
B. L. Bars
A. Bellet
Marc Tommasi
Erick Lavoie
Anne-Marie Kermarrec
FedML
419
42
0
09 Apr 2022
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!
International Conference on Machine Learning (ICML), 2022
Konstantin Mishchenko
Grigory Malinovsky
Sebastian U. Stich
Peter Richtárik
383
198
0
18 Feb 2022
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning
Neural Information Processing Systems (NeurIPS), 2022
Anastasia Koloskova
Tao Lin
Sebastian U. Stich
321
127
0
08 Feb 2022
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Bicheng Ying
Kun Yuan
Hanbin Hu
Yiming Chen
W. Yin
FedML
302
31
0
08 Nov 2021
Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying
Kun Yuan
Yiming Chen
Hanbin Hu
Pan Pan
W. Yin
FedML
262
109
0
26 Oct 2021
A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning
Sulaiman A. Alghunaim
Kun Yuan
262
84
0
19 Oct 2021
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data
International Conference on Machine Learning (ICML), 2021
Tao Lin
Sai Praneeth Karimireddy
Sebastian U. Stich
Martin Jaggi
FedML
390
115
0
09 Feb 2021
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