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Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL).
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Fast linear iterations for distributed averaging
L. Xiao and S. Boyd · 2003
Earlier work this paper cites.
Distributed subgradient methods for multi-agent optimization
A. Nedic and A. Ozdaglar · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Dual averaging for distributed optimization: Convergence analysis and network scaling
J. C. Duchi, A. Agarwal, and M. J. Wainwright · 2011
Earlier work this paper cites.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Y. Ng · 2012
Earlier work this paper cites.
Distributed alternating direction method of multipliers
E. Wei and A. Ozdaglar · 2012
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Communication/computation tradeoffs in consensus-based distributed optimization
K. Tsianos, S. Lawlor, and M. Rabbat · 2012
Earlier work this paper cites.
Making gradient descent optimal for strongly convex stochastic optimization
A. Rakhlin, O. Shamir, and K. Sridharan · 2012
Earlier work this paper cites.
Matrix analysis
R. A. Horn and C. R. Johnson · 2012
Earlier work this paper cites.
Asynchronous parallel stochastic gradient for nonconvex optimization
X. Lian, Y. Huang, Y. Li, and J. Liu · 2015
Earlier work this paper cites.
Fog and iot: An overview of research opportunities
M. Chiang and T. Zhang · 2016
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
Earlier work this paper cites.
On the convergence of decentralized gradient descent
K. Yuan, Q. Ling, and W. Yin · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
Earlier work this paper cites.
Federated multi-task learning
V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar · 2017
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Decentralized collaborative learning of personalized models over networks
P. Vanhaesebrouck, A. Bellet, and M. Tommasi · 2017
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Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu · 2017
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Decentralized consensus optimization with asynchrony and delays
T. Wu, K. Yuan, Q. Ling, W. Yin, and A. H. Sayed · 2017
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Collaborative deep learning in fixed topology networks
Z. Jiang, A. Balu, C. Hegde, and S. Sarkar · 2017
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Decentralized consensus algorithm with delayed and stochastic gradients
B. Sirb and X. Ye · 2018
Communication-censored linearized admm for decentralized consensus optimization
W. Li, Y. Liu, Z. Tian, and Q. Ling · 2019
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Byrdie: Byzantine-resilient distributed coordinate descent for decentralized learning
Z. Yang and W. U. Bajwa · 2019
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Cooperative sgd: A unified framework for the design and analysis of communication-efficient sgd algorithms
J. Wang and G. Joshi · 2019
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Communication-efficient local decentralized sgd methods
X. Li, W. Yang, S. Wang, and Z. Zhang · 2019
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Decentralized stochastic optimization and gossip algorithms with compressed communication
A. Koloskova, S. Stich, and M. Jaggi · 2019
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Local sgd converges fast and communicates little
S. U. Stich · 2018
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A linear speedup analysis of distributed deep learning with sparse and quantized communication
P. Jiang and G. Agrawal · 2018
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Optimization methods for large-scale machine learning
L. Bottou, F. E. Curtis, and J. Nocedal · 2018
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Adaptive federated learning in resource constrained edge computing systems
S. Wang, T. Tuor, T. Salonidis, K. K. Leung, C. Makaya, T. He, and K. Chan · 2019
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al · 2019
Cited alongside, same era.
Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning
H. Yu, S. Yang, and S. Zhu · 2019
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On the linear speedup analysis of communication efficient momentum sgd for distributed non-convex optimization
H. Yu, R. Jin, and S. Yang · 2019
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Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2020
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On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2020
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Optimizing federated learning on non-iid data with reinforcement learning
H. Wang, Z. Kaplan, D. Niu, and B. Li · 2020
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Accelerating federated learning via momentum gradient descent
W. Liu, L. Chen, Y. Chen, and W. Zhang · 2020
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2020
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Robust federated learning with noisy communication
F. Ang, L. Chen, N. Zhao, Y. Chen, W. Wang, and F. R. Yu · 2020
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A computation-efficient decentralized algorithm for composite constrained optimization
Q. Lü, X. Liao, H. Li, and T. Huang · 2020
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A newton tracking algorithm with exact linear convergence for decentralized consensus optimization
J. Zhang, Q. Ling, and A. M. So · 2021
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