Client selection in federated learning based on gradients importance

Author:

Marnissi Ouiame,Hammouti Hajar El,Bergou El Houcine

Publisher

AIP Publishing

Reference18 articles.

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3. J. Konečny`, 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 (2016).

4. A. F. Aji and K. Heafield, “Sparse communication for distributed gradient descent,” arXiv preprint arXiv:1704.05021 (2017).

5. H. Xu, C.-Y. Ho, A. M. Abdelmoniem, A. Dutta, E. H. Bergou, K. Karatsenidis, M. Canini, and P. Kalnis, “Grace: A compressed communication framework for distributed machine learning,” in 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS) (2021) pp. 561–572.

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