Evaluation of Client Selection Mechanisms in Vehicular Federated Learning Environments with Client Failures

• John Sousa UFPA
• Eduardo Ribeiro UFPA
• Lucas Bastos UFPA
• Denis Rosário UFPA
• Allan M. de Sousa UNICAMP
• Eduardo Cerqueira UFPA

DOI: https://doi.org/10.5753/sbrc.2024.1486

Resumo

Federated Learning (FL) emerges as a promising solution to enable collaborative model training for autonomous vehicles while preserving privacy and communication overhead issues. An efficient selection of clients to participate in the training process is still challenging, especially in scenarios with statistical heterogeneity of data distribution and client failure events. Client failure is an uncontrollable event in the training process that reduces accuracy, convergence, and speed. Therefore, investigating the performance of client selection mechanisms in this scenario is crucial. This paper presents a reliability and robustness analysis of entropy-based client selection mechanisms in FL environments with client failure. The results demonstrated that entropy-based selection outperformed the other methods regarding training loss, accuracy, and AUC, particularly in high client dropout scenarios. These findings show the importance of considering entropy data for client selection when addressing the challenges posed by client failure in FL scenarios.

Referências

de Souza, A. M., Maciel, F., da Costa, J. B., Bittencourt, L. F., Cerqueira, E., Loureiro, A. A., and Villas, L. A. (2024). Adaptive client selection with personalization for communication efficient federated learning. Ad Hoc Networks, 157:103462.

Fantauzzo, L., Fanı̀, E., Caldarola, D., Tavera, A., Cermelli, F., Ciccone, M., and Caputo, B. (2022). Feddrive: Generalizing federated learning to semantic segmentation in autonomous driving. In prooceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11504–11511. IEEE.

Huang, T., Lin, W., Shen, L., Li, K., and Zomaya, A. Y. (2022). Stochastic client selection for federated learning with volatile clients. IEEE Internet of Things Journal, 9(20):20055–20070.

Jallepalli, D., Ravikumar, N. C., Badarinath, P. V., Uchil, S., and Suresh, M. A. (2021). Federated learning for object detection in autonomous vehicles. In prooceedings of the IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), pages 107–114.

Lian, X., Yuan, B., Zhu, X., Wang, Y., et al. (2022). Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 3288–3298.

Nguyen, A., Do, T., Tran, M., Nguyen, B. X., Duong, C., Phan, T., Tjiputra, E., and Tran, Q. D. (2022). Deep federated learning for autonomous driving. In proceedings of the IEEE Intelligent Vehicles Symposium (IV), pages 1824–1830. IEEE.

Pannu, G. S., Ucar, S., Higuchi, T., Altintas, O., and Dressler, F. (2021). Dwell time estimation at intersections for improved vehicular micro cloud operations. Ad Hoc Networks, 122:102606.

Pervej, M. F., Jin, R., and Dai, H. (2023). Resource Constrained Vehicular Edge Federated Learning With Highly Mobile Connected Vehicles. IEEE Journal on Selected Areas in Communications, 41(6):1825–1844.

Shanmugarasa, Y., young Paik, H., Kanhere, S. S., and Zhu, L. (2023). A systematic review of federated learning from clients’ perspective: challenges and solutions, volume 56. Springer Netherlands.

Sousa, J. L. R., Lobato, W., Rosário, D., Cerqueira, E., and Villas, L. A. (2023). Entropy-based client selection mechanism for vehicular federated environments. In Proceedings of the 22nd Workshop on Performance of Computer and Communication Systems (WPERFORMANCE), pages 37–48. SBC.

Souza, A., Bittencourt, L., Cerqueira, E., Loureiro, A., and Villas, L. (2023). Dispositivos, eu escolho vocês: Seleção de clientes adaptativa para comunicação eficiente em aprendizado federado. In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), pages 1–14, Porto Alegre, RS, Brasil. SBC.

Stergiou, K. D., Psannis, K. E., Vitsas, V., and Ishibashi, Y. (2022). A federated learning approach for enhancing autonomous vehicles image recognition. In 2022 4th International Conference on Computer Communication and the Internet (ICCCI), pages 87–90. IEEE.

Sun, Y., Member, G. S., Mao, Y., and Zhang, J. (2023). MimiC:Combating Client Dropouts in Federated Learning by Mimicking Central Updates. arXiv:2306.12212v3, pages 1–17.

Wang, H. and Xu, J. (2023). Combating Client Dropout in Federated Learning via Friend Model Substitution. arXiv preprint arXiv:2205.13222.

Xiong, Y., Wang, R., Cheng, M., Yu, F., and Hsieh, C.-J. (2023). Feddm: Iterative distribution matching for communication-efficient federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16323–16332.

Yuan, L., Su, L., and Wang, Z. (2023). Federated transfer-ordered-personalized learning for driver monitoring application. IEEE Internet of Things Journal.

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y. (2021a). A survey on federated learning. Knowledge-Based Systems, 216:106775.

Zhang, H., Bosch, J., and Olsson, H. H. (2021b). End-to-end federated learning for autonomous driving vehicles. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), pages 1–8.

Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., and Guan, H. (2023a). Fedala: Adaptive local aggregation for personalized federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 11237–11244.

Zhang, X., Chang, Z., Hu, T., Chen, W., Zhang, X., and Min, G. (2023b). Vehicle Selection and Resource Allocation for Federated Learning-Assisted Vehicular Network. IEEE Transactions on Mobile Computing, PP:1–12.

Zhang, X., Liu, J., Hu, T., Chang, Z., Zhang, Y., and Min, G. (2023c). Federated learning-assisted vehicular edge computing: Architecture and research directions. IEEE Vehicular Technology Magazine, pages 2–11.

Zhu, H., Xu, J., Liu, S., and Jin, Y. (2021). Federated learning on non-IID data: A survey. Neurocomputing, 465:371–390.