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Existing theory predicts that data heterogeneity will degrade the performance of the Federated Averaging (FedAvg) algorithm in federated learning.
On the convergence of decentralized gradient descent
K. Yuan, Q. Ling, and W. Yin · 2016
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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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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, et al · 2017
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Stochastic gradient push for distributed deep learning
M. Assran, N. Loizou, N. Ballas, and M. Rabbat · 2018
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J. Wang and G. Joshi · 2018
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On the convergence properties of a k-step averaging stochastic gradient descent algorithm for nonconvex optimization
F. Zhou and G. Cong · 2018
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Sgd: General analysis and improved rates
R. M. Gower, N. Loizou, X. Qian, A. Sailanbayev, E. Shulgin, and P. Richtárik · 2019
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On the convergence of local descent methods in federated learning
F. Haddadpour and M. Mahdavi · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
T.-M. H. Hsu, H. Qi, and M. Brown · 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
Earlier work this paper cites.
On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 2019
Earlier work this paper cites.
On the convergence of adam and beyond
S. J. Reddi, S. Kale, and S. Kumar · 2019
Cited alongside, same era.
Local SGD converges fast and communicates little
S. U. Stich · 2019
Cited alongside, same era.
Unified optimal analysis of the (stochastic) gradient method
S. U. Stich · 2019
Cited alongside, same era.
On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
H. Yu, R. Jin, and S. Yang · 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
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor · 2020
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SlowMo: Improving communication-efficient distributed SGD with slow momentum
J. Wang, V. Tantia, N. Ballas, and M. Rabbat · 2020
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Minibatch vs local sgd for heterogeneous distributed learning
B. Woodworth, K. K. Patel, and N. Srebro · 2020
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Is local SGD better than minibatch SGD?
B. Woodworth, K. K. Patel, S. U. Stich, Z. Dai, B. Bullins, H. B. McMahan, O. Shamir, and N. Srebro · 2020
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Achieving linear speedup with partial worker participation in non-iid federated learning
H. Yang, M. Fang, and J. Liu · 2020
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SCAFFOLD: Stochastic controlled averaging for on-device federated learning
S. P. Karimireddy, S. Kale, M. Mohri, S. J. Reddi, S. U. Stich, and A. T. Suresh · 2020
Cited alongside, same era.
Tighter theory for local SGD on identical and heterogeneous data
A. Khaled, K. Mishchenko, and P. Richtárik · 2020
Cited alongside, same era.
A unified theory of decentralized SGD with changing topology and local updates
A. Koloskova, N. Loizou, S. Boreiri, M. Jaggi, and S. U. Stich · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2020
Cited alongside, same era.
From local sgd to local fixed-point methods for federated learning
G. Malinovskiy, D. Kovalev, E. Gasanov, L. Condat, and P. Richtarik · 2020
Cited alongside, same era.
Adaptive federated optimization
S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečnỳ, S. Kumar, and H. B. McMahan · 2020
Cited alongside, same era.
Z. Charles, Z. Garrett, Z. Huo, S. Shmulyian, and V. Smith · 2021
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Convergence and accuracy trade-offs in federated learning and meta-learning
Z. Charles and J. Konečnỳ · 2021
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Sharp bounds for federated averaging (local sgd) and continuous perspective
M. Glasgow, H. Yuan, and T. Ma · 2021
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A field guide to federated optimization
J. Wang, Z. Charles, Z. Xu, G. Joshi, H. B. McMahan, M. Al-Shedivat, G. Andrew, S. Avestimehr, K. Daly, D. Data, et al · 2021
Later among the works it cites.
Iterated vector fields and conservatism, with applications to federated learning
Z. Charles and K. Rush · 2022
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