Federated Learning Enabled Green Edge Computing System for IIoT Applications
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-68005-2_2
Reference16 articles.
1. Peng, K., Huang, H., Zhao, B., Jolfaei, A., Xu, X., Bilal, M.: Intelligent computation offloading and resource allocation in IIoT with end-edge-cloud computing using NSGA-III. IEEE Trans. Netw. Sci. Eng. 10(5), 3032–3046 (2022)
2. Kim, D.-Y., Lee, S., Kim, M., Kim, S.: Edge cloud selection in mobile edge computing (MEC)-aided applications for industrial internet of things (IIoT) services. Comput. Syst. Sci. Eng. 47(2) (2023)
3. Kumar, R., Agrawal, N.: Analysis of multi-dimensional industrial IoT (IIoT) data in edge-fog-cloud based architectural frameworks: a survey on current state and research challenges. J. Ind. In. Integr. 100504 (2023)
4. Shi, Y., Yi, C., Chen, B., Yang, C., Zhu, K., Cai, J.: Joint online optimization of data sampling rate and preprocessing mode for edge-cloud collaboration-enabled industrial IoT. IEEE Internet Things J. 9(17), 16402–16417 (2022)
5. Fan, W., Li, S., Liu, J., Su, Y., Wu, F., Liu, Y.: Joint task offloading and resource allocation for accuracy-aware machine-learning-based IIoT applications. IEEE Internet Things J. 10(4), 3305–3321 (2022)
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