Enhancing Efficiency and Privacy of Intelligent Public Transportation Systems Through Federated Learning and EdgeAI
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60796-7_15
Reference13 articles.
1. Amato, F., Di Martino, S., Mazzocca, N., Nardone, D., di Torrepadula, F.R., Sannino, P.: Bus passenger load prediction: challenges from an industrial experience. In: Karimipour, F., Storandt, S. (eds.) International Symposium on Web and Wireless Geographical Information Systems, pp. 93–107. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06245-2_9
2. Chen, J., Ran, X.: Deep learning with edge computing: a review. Proc. IEEE 107(8), 1655–1674 (2019). https://doi.org/10.1109/JPROC.2019.2921977
3. Cheng, X., Rao, Z., Chen, Y., Zhang, Q.: Explaining knowledge distillation by quantifying the knowledge. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12925–12935 (2020)
4. Deng, L., Li, G., Han, S., Shi, L., Xie, Y.: Model compression and hardware acceleration for neural networks: a comprehensive survey. Proc. IEEE 108(4), 485–532 (2020)
5. Elkosantini, S., Darmoul, S.: Intelligent public transportation systems: a review of architectures and enabling technologies. In: 2013 International Conference on Advanced Logistics and Transport, pp. 233–238. IEEE (2013)
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