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Federated Learning is a distributed machine learning approach which enables model training without data sharing.
On the convergence of fedavg on non-iid data
X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang · 1907
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Mime: Mimicking centralized stochastic algorithms in federated learning
S. P. Karimireddy, M. Jaggi, S. Kale, M. Mohri, S. J. Reddi, S. U. Stich, and A. T. Suresh · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Parallelized stochastic gradient descent
M. Zinkevich, M. Weimer, A. J. Smola, and L. Li · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, and E. Chu · 2011
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Large scale distributed deep networks
J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, et al · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Convex optimization: Algorithms and complexity
S. Bubeck · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Communication-efficient distributed optimization using an approximate newton-type method
O. Shamir, N. Srebro, and T. Zhang · 2014
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon · 2016
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Regulation eu 2016/679 of the european parliament and of the council of 27 april 2016
G. D. P. Regulation · 2016
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Communication-efficient learning of deep networks from decentralized data
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas · 2017
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Federated optimization in heterogeneous networks
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith · 2018
Cited alongside, same era.
Local sgd converges fast and communicates little
S. U. Stich · 2018
Cited alongside, same era.
Group normalization
Y. Wu and K. He · 2018
Cited alongside, same era.
Federated learning with non-iid data
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra · 2018
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
T.-M. H. Hsu, H. Qi, and M. Brown · 2019
Cited alongside, same era.
The non-iid data quagmire of decentralized machine learning
K. Hsieh, A. Phanishayee, O. Mutlu, and P. Gibbons · 2020
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Faster on-device training using new federated momentum algorithm
Z. Huo, Q. Yang, B. Gu, L. C. Huang, et al · 2020
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Federated learning: Challenges, methods, and future directions
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith · 2020
Later among the works it cites.
Accelerating federated learning via momentum gradient descent
W. Liu, L. Chen, Y. Chen, and W. Zhang · 2020
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Fedsplit: An algorithmic framework for fast federated optimization
R. Pathak and M. J. Wainwright · 2020
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Improving federated learning personalization via model agnostic meta learning
Y. Jiang, J. Konečnỳ, K. Rush, and S. Kannan · 2019
Cited alongside, same era.
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.
Feddane: A federated newton-type method
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smithy · 2019
Cited alongside, same era.
Variance reduced local sgd with lower communication complexity
X. Liang, S. Shen, J. Liu, Z. Pan, E. Chen, and Y. Cheng · 2019
Cited alongside, same era.
Slowmo: Improving communication-efficient distributed sgd with slow momentum
J. Wang, V. Tantia, N. Ballas, and M. Rabbat · 2019
Cited alongside, same era.
Local adaalter: Communication-efficient stochastic gradient descent with adaptive learning rates
C. Xie, O. Koyejo, I. Gupta, and H. Lin · 2019
Cited alongside, same era.
Bayesian nonparametric federated learning of neural networks
M. Yurochkin, M. Agarwal, S. Ghosh, K. Greenewald, N. Hoang, and Y. Khazaeni · 2019
Cited alongside, same era.
S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konečnỳ, S. Kumar, and H. B. McMahan · 2020
Later among the works it cites.
Effective federated adaptive gradient methods with non-iid decentralized data
Q. Tong, G. Liang, and J. Bi · 2020
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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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Federated accelerated stochastic gradient descent
H. Yuan and T. Ma · 2020
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Fedpd: A federated learning framework with optimal rates and adaptivity to non-iid data
X. Zhang, M. Hong, S. Dhople, W. Yin, and Y. Liu · 2020
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Federated learning based on dynamic regularization
D. A. E. Acar, Y. Zhao, R. M. Navarro, M. Mattina, P. N. Whatmough, and V. Saligrama · 2021
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Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data
T. Lin, S. P. Karimireddy, S. U. Stich, and M. Jaggi · 2021
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speedtest.net, 2021
speedtest.net · 2021
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