Towards an Efficient Client Selection System for Federated Learning-Reference-Cited by-同舟云学术

Towards an Efficient Client Selection System for Federated Learning

Published:2022 Issue: Volume: Page:13-21
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Ding Guanyu,Li Zhengxiong,Wu Yusen,Yang Xiaokun,Aliasgari Mehrdad,Xu Hailu

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-23498-9_2

Reference17 articles.

1. Balakrishnan, R., Li, T., Zhou, T., Himayat, N., Smith, V., Bilmes, J.: Diverse client selection for federated learning: submodularity and convergence analysis. In: ICML 2021 International Workshop on Federated Learning for User Privacy and Data Confidentiality (2021)

2. Bonawitz, K., et al.: Towards federated learning at scale: system design. Proc. Mach. Learn. Syst. 1, 374–388 (2019)

3. Castro, M., Druschel, P., Kermarrec, A.M., Rowstron, A.I.: Scribe: a large-scale and decentralized application-level multicast infrastructure. IEEE J. Sel. Areas Commun. 20(8), 1489–1499 (2002)

4. Cho, Y.J., Wang, J., Joshi, G.: Client selection in federated learning: convergence analysis and power-of-choice selection strategies. arXiv preprint arXiv:2010.01243 (2020)

5. Cho, Y.J., Wang, J., Joshi, G.: Towards understanding biased client selection in federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 10351–10375. PMLR (2022)

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