Leader-Assisted Client Selection for Federated Learning in IoT via the Cooperation of Nearby Devices

Author:

Aiche MohamedORCID,Ouchani SamirORCID,Bouarfa HafidaORCID

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Wahab, O.A., Mourad, A., Otrok, H., Taleb, T.: Federated machine learning: survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Commun. Surv. Tutorials 23(2), 1342–1397 (2021). https://doi.org/10.1109/COMST.2021.3058573

2. Karimipour, H., Derakhshan, F. (eds.): AI-Enabled Threat Detection and Security Analysis for Industrial IoT. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76613-9

3. McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 54, pp. 1273–1282. PMLR (2017). https://proceedings.mlr.press/v54/mcmahan17a.html

4. Kairouz, P., et al.: Advances and open problems in federated learning. Foundations Trends® Mach. Learn. 14(1–2), 1–210 (2021)

5. Li, Q., et al.: A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Trans. Knowl. Data Eng. (2021)

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