An Automated Network Slicing at Edge with Software Defined Networking and Network Function Virtualization: A Federated Learning Approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10450-z.pdf
Reference27 articles.
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2. Khan, L. U., Yaqoob, I., Tran, N. H., Han, Z., & Hong, C. S. (2020). Network slicing: Recent advances, taxonomy, requirements, and open research challenges. IEEE Access, 8, 36009–36028. https://doi.org/10.1109/ACCESS.2020.2975072
3. Niknam, S., Dhillon, H. S., & Reed, J. H. (2020). Federated learning for wireless communications: Motivation, opportunities, and challenges. IEEE Communications Magazine, 58(6), 46–51.
4. Tonini, F., Natalino, C., Furdek, M., Raffaelli, C., & Monti, P. (2020). Network slicing automation: Challenges and benefits. In 2020 international conference on optical network design and modeling (ONDM). IEEE.
5. Wang, X., Han, Y., Wang, C., Qiyang Zhao, X., & Chen, M. C. (2019). In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network, 33(5), 156–165. https://doi.org/10.1109/MNET.2019.1800286
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