Enhancing Security and Efficiency: A Lightweight Federated Learning Approach
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57916-5_30
Reference30 articles.
1. Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., Guizani, M.: A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476–5497 (2021)
2. Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., Zhang, W.: A survey on federated learning: challenges and applications. Int. J. Mach. Learn. Cybern. 14(2), 513–535 (2023)
3. Ratnayake, H., Chen, L., Ding, X.: A review of federated learning: taxonomy, privacy and future directions. J. Intell. Inf. Syst. 61, 1–27 (2023)
4. Yin, X., Zhu, Y., Hu, J.: A taxonomy, review, and future directions, a comprehensive survey of privacy-preserving federated learning. ACM Comput. Surv. 54, 1–36 (2021)
5. Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M.V., Herrera, F., Martínez-Cámara, E.: Survey on federated learning threats: concepts, taxonomy on attacks and defences, experimental study and challenges. Inf. Fusion 90, 148–173 (2023)
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