RuCIL: Enabling Privacy-Enhanced Edge Computing for Federated Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51826-3_3
Reference18 articles.
1. Ju, W., et al.: A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, vol. 14, no. 2, Springer Science+Business Media, Nov. 2022, pp. 513–35. https://doi.org/10.1007/s13042-022-01647-y
2. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019). https://doi.org/10.1109/JPROC.2019.2918951
3. Brecko, A., Kajati, E., Koziorek, J., Zolotova, I.: Federated learning for edge computing: a survey. Appl. Sci. 12, 9124 (2022). https://doi.org/10.3390/app12189124
4. Xia, Qi, et al.: A survey of federated learning for edge computing: research problems and solutions. High-Confidence Comput., 1(1), 100008 Elsevier BV, June (2021) . https://doi.org/10.1016/j.hcc.2021.100008
5. Abreha, H.G., Hayajneh, M., Serhani, M.A.: Federated learning in edge computing: a systematic survey. Sensors. 22, 450 (2022). https://doi.org/10.3390/s22020450
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