A General Theory for Client Sampling in Federated Learning

Author:

Fraboni Yann,Vidal Richard,Kameni Laetitia,Lorenzi Marco

Publisher

Springer International Publishing

Reference34 articles.

1. Acar, D.A.E., Zhao, Y., Matas, R., Mattina, M., Whatmough, P., Saligrama, V.: Federated learning based on dynamic regularization. In: International Conference on Learning Representations (2021)

2. Basu, D., Data, D., Karakus, C., Diggavi, S.: Qsparse-local-SGD: distributed SGD with quantization, sparsification and local computations. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates Inc. (2019)

3. Caldas, S., et al.: LEAF: a benchmark for federated settings. In: NeurIPS, pp. 1–9 (2018)

4. Chen, W., Horvath, S., Richtarik, P.: Optimal client sampling for federated learning. In: Workshop in NeurIPS: Privacy Preserving Machine Learning (2020)

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

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