Built on

Y. LeCun, “The mnist database of handwritten digits,” http://yann. lecun. com/exdb/mnist/
1998
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E. del Barrio, E. Giné, and C. Matrán, “Central limit theorems for the wasserstein distance between the empirical and the true distributions,” Annals of Probability
1999
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Springer Science & Business Media, 2008
C. Villani, Optimal transport: old and new · 2008
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A. Krizhevsky, G. Hinton, et al
2009
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J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Privacy aware learning,” Journal of the ACM (JACM)
2014
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J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492
Original
2016
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C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning
2017
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B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
2017
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H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning differentially private recurrent language models,” arXiv preprint arXiv:1710.06963
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2017
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Z. Li, F. Zhou, F. Chen, and H. Li, “Meta-SGD: Learning to learn quickly for few-shot learning,” arXiv preprint arXiv:1707.09835
Original
2017
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V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems
2017
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N. Agarwal, A. T. Suresh, F. X. X. Yu, S. Kumar, and B. McMahan, “cpsgd: Communication-efficient and differentially-private distributed sgd,” in Advances in Neural Information Processing Systems
2018
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S. U. Stich, “Local sgd converges fast and communicates little,” arXiv preprint arXiv:1805.09767
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2018
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J. Wang and G. Joshi, “Cooperative sgd: A unified framework for the design and analysis of communication-efficient sgd algorithms,” arXiv preprint arXiv:1808.07576
Original
2018
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F. Zhou and G. Cong, “On the convergence properties of a k-step averaging stochastic gradient descent algorithm for nonconvex optimization,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence
2018
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Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582
Original
2018
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A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith, “On the convergence of federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127
Original
2018
Cited alongside, same era.
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths, “Recasting gradient-based meta-learning as hierarchical bayes,” in International Conference on Learning Representations
2018
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A. Nichol, J. Achiam, and J. Schulman, “On first-order meta-learning algorithms,” arXiv preprint arXiv:1803.02999
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Then

T. Li, M. Sanjabi, and V. Smith, “Fair resource allocation in federated learning,” arXiv preprint arXiv:1905.10497
Original
2019
Later among the works it cites.
M. Khodak, M.-F. F. Balcan, and A. S. Talwalkar, “Adaptive gradient-based meta-learning methods,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
J. Li, M. Khodak, S. Caldas, and A. Talwalkar, “Differentially private meta-learning,” arXiv preprint arXiv:1909.05830
Original
2019
Later among the works it cites.
Y. Arjevani, Y. Carmon, J. C. Duchi, D. J. Foster, N. Srebro, and B. Woodworth, “Lower bounds for non-convex stochastic optimization,” arXiv preprint arXiv:1912.02365
Original
2019
Later among the works it cites.
J. Langelaar, “Mnist neural network training and testing,” MATLAB Central File Exchange

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2020

Personalized Federated Learning: A Meta-Learning Approach

Fallah, Alireza, Mokhtari, Aryan, Ozdaglar, Asuman

Understand

In Federated Learning, we aim to train models across multiple computing units (users), while users can only communicate with a common central server, without exchanging their data samples.

This mechanism exploits the computational power of all users and allows users to obtain a richer model as their models are trained over a larger set of data points.
However, this scheme only develops a common output for all the users, and, therefore, it does not adapt the model to each user.
This is an important missing feature, especially given the heterogeneity of the underlying data distribution for various users.

Built on

Y. LeCun, “The mnist database of handwritten digits,” http://yann. lecun. com/exdb/mnist/
1998
Earlier work this paper cites.
E. del Barrio, E. Giné, and C. Matrán, “Central limit theorems for the wasserstein distance between the empirical and the true distributions,” Annals of Probability
1999
Earlier work this paper cites.
Springer Science & Business Media, 2008
C. Villani, Optimal transport: old and new · 2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, et al
2009
Earlier work this paper cites.
J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Privacy aware learning,” Journal of the ACM (JACM)
2014
Earlier work this paper cites.
J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492
Original
2016
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning
2017
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
2017
Earlier work this paper cites.
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning differentially private recurrent language models,” arXiv preprint arXiv:1710.06963
Original
2017
Earlier work this paper cites.
Z. Li, F. Zhou, F. Chen, and H. Li, “Meta-SGD: Learning to learn quickly for few-shot learning,” arXiv preprint arXiv:1707.09835
Original
2017
Earlier work this paper cites.
V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems
2017
Earlier work this paper cites.
N. Agarwal, A. T. Suresh, F. X. X. Yu, S. Kumar, and B. McMahan, “cpsgd: Communication-efficient and differentially-private distributed sgd,” in Advances in Neural Information Processing Systems
2018
Earlier work this paper cites.
S. U. Stich, “Local sgd converges fast and communicates little,” arXiv preprint arXiv:1805.09767
Original
2018
Earlier work this paper cites.
J. Wang and G. Joshi, “Cooperative sgd: A unified framework for the design and analysis of communication-efficient sgd algorithms,” arXiv preprint arXiv:1808.07576
Original
2018
Earlier work this paper cites.
F. Zhou and G. Cong, “On the convergence properties of a k-step averaging stochastic gradient descent algorithm for nonconvex optimization,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence
2018
Earlier work this paper cites.
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582
Original
2018
Earlier work this paper cites.

Similar

A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith, “On the convergence of federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127
Original
2018
Cited alongside, same era.
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths, “Recasting gradient-based meta-learning as hierarchical bayes,” in International Conference on Learning Representations
2018
Cited alongside, same era.
A. Nichol, J. Achiam, and J. Schulman, “On first-order meta-learning algorithms,” arXiv preprint arXiv:1803.02999
Original
2018
Cited alongside, same era.
F. Chen, M. Luo, Z. Dong, Z. Li, and X. He, “Federated meta-learning with fast convergence and efficient communication,” arXiv preprint arXiv:1802.07876
Original
2018
Cited alongside, same era.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings, et al

Then

T. Li, M. Sanjabi, and V. Smith, “Fair resource allocation in federated learning,” arXiv preprint arXiv:1905.10497
Original
2019
Later among the works it cites.
M. Khodak, M.-F. F. Balcan, and A. S. Talwalkar, “Adaptive gradient-based meta-learning methods,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
J. Li, M. Khodak, S. Caldas, and A. Talwalkar, “Differentially private meta-learning,” arXiv preprint arXiv:1909.05830
Original
2019
Later among the works it cites.
Y. Arjevani, Y. Carmon, J. C. Duchi, D. J. Foster, N. Srebro, and B. Woodworth, “Lower bounds for non-convex stochastic optimization,” arXiv preprint arXiv:1912.02365
Original
2019
Later among the works it cites.
J. Langelaar, “Mnist neural network training and testing,” MATLAB Central File Exchange

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Original

2019

Cited alongside, same era.

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Later among the works it cites.

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