Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning

R. Jain, D. Chiu, and W. Hawe, “A quantitative measure of fairness and discrimination for resource allocation in shared computer systems,” vol. cs.NI/9809099, 1998. [Online]. Available: https://arxiv.org/abs/cs/9809099

1998

Earlier work this paper cites.

A. Garivier and E. Moulines, “On upper-confidence bound policies for non-stationary bandit problems,” vol. abs/0805.3415, 2008. [Online]. Available: http://arxiv.org/abs/0805.3415

Original

2008

Earlier work this paper cites.

Y. J. Cho, J. Wang, and G. Joshi, “Client selection in federated learning: Convergence analysis and power-of-choice selection strategies,” vol. abs/2010.01243, 2020. [Online]. Available: http://arxiv.org/abs/2010.01243

Original

2010

Earlier work this paper cites.

T. Lan, D. Kao, M. Chiang, and A. Sabharwal, “An axiomatic theory of fairness in network resource allocation,” in 2010 Proceedings IEEE INFOCOM , 2010, pp. 1–9

2010

Earlier work this paper cites.

C. Joe-Wong, S. Sen, T. Lan, and M. Chiang, “Multi-resource allocation: Fairness-efficiency tradeoffs in a unifying framework,” in 2012 Proceedings IEEE INFOCOM , 2012, pp. 1206–1214

2012

Earlier work this paper cites.

S. Bubeck and N. Cesa-Bianchi, “Regret analysis of stochastic and nonstochastic multi-armed bandit problems,” Foundations and Trends in Machine Learning , vol. 5, no. 1, pp. 1–122, 2012

2012

Earlier work this paper cites.

H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” International Conference on Artificial Intelligenece and Statistics (AISTATS) , Apr. 2017. [Online]. Available: https://arxiv.org/abs/1602.05629

Original

2017

Earlier work this paper cites.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” https://arxiv.org/abs/1708.07747 , aug 2017

Original

2017

Cited alongside, same era.

S. U. Stich, “Local SGD converges fast and communicates little,” arXiv preprint arXiv:1805.09767 , 2018

Original

2018

Cited alongside, same era.

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 , 2018. [Online]. Available: https://arxiv.org/abs/1808.07576

Original

2018

Cited alongside, same era.

H. Yu, S. Yang, and S. Zhu, “Parallel restarted SGD for non-convex optimization with faster convergence and less communication,” arXiv preprint arXiv:1807.06629 , 2018

Original

2018

Cited alongside, same era.

J. Goetz, K. Malik, D. Bui, S. Moon, H. Liu, and A. Kumar, “Active federated learning,” ArXiv , 2019

2019

Later among the works it cites.

T. Li, M. Sanjabi, and V. Smith, “Fair resource allocation in federated learning,” arXiv preprint arXiv:1905.10497 , 2019

Original

2019

Later among the works it cites.

M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in Proceedings of the 36th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97. Long Beach, California, USA: PMLR, 09–15 Jun 2019, pp. 4615–4625

2019

Later among the works it cites.

A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith, “Federated optimization for heterogeneous networks,” https://arxiv.org/abs/1812.06127 , Jan. 2019

Original

2019

Later among the works it cites.