SUMO Simulations for Federated Learning in Communicating Autonomous Vehicles

A Survey on Efficiency and Security




federated learning, communicating vehicles, efficiency, security


In transportation, a vehicle's route is one of the most private information. However, to mutually learn some phenomena in a city, for example, parking lot occupancies, we might have to reveal information about it. In this paper, we focus on assessing the privacy loss in a vehicular federated machine learning system. For the analysis, we used the Monaco SUMO Traffic Scenario (MoST). We also used the simulation inputs as statistical data to calculate privacy loss metrics. Results show that a vehicular federated machine learning system may pose a smaller privacy threat than individual learning, but its performance is lower compared to a centralized learning approach.

Due to the vast amount of data and processing time, we also describe a method to build a Docker image of SUMO together with a software client-server architecture for SUMO-based learning systems on multiple computers.


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W. Y. B. Lim, N. C. Luong, D. T. Hoang, et al., “Federated learning in mobile edge networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031–2063, 2020. DOI: DOI:

L. Codeca and J. H¨ arri, “Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS,” in SUMO 2018, SUMO User Conference, Simulating Autonomous and Intermodal Transport Systems, May 14-16, 2018, Berlin, Germany, Berlin, GERMANY, May 2018.

P. A. Lopez, M. Behrisch, L. Bieker-Walz, et al., “Microscopic traffic simulation using sumo,” in The 21st IEEE International Conference on Intelligent Transportation Systems, IEEE, 2018. [Online]. Available: DOI:

F. Bock, S. Di Martino, and A. Origlia, “Smart parking: Using a crowd of taxis to sense on-street parking space availability,” IEEE Transactions on Intelligent Transportation Systems,vol. 21, no. 2, pp. 496–508, 2020. DOI: DOI:

H. Khayyam, B. Javadi, M. Jalili, and R. N. Jazar, “Artificial intelligence and internet of things for autonomous vehicles,” R. N. Jazar and L. Dai, Eds., pp. 39–68, 2020. DOI: DOI:

S. Iqbal, P. Ball, M. H. Kamarudin, and A. Bradley, “Simulating malicious attacks on VANETs for connected and autonomous vehicle cybersecurity: A machine learning dataset,” in 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal, July 20-22. 2022., 2022, pp. 332–337. DOI: DOI:

P. Sharma, D. Austin, and H. Liu, “Attacks on machine learning: Adversarial examples in connected and autonomous vehicles,” in 2019 IEEE International Symposium on Technologies for Homeland Security (HST), 2019, pp. 1–7. DOI: DOI:

M. T¨ urko˘ glu, H. Polat, C. Koc¸ak, and O. Polat, “Recognition of DDoS attacks on SDVANET based on combination of hyperparameter optimization and feature selection,”Expert Systems with Applications, vol. 203, p. 117 500, 2022, ISSN: 0957-4174. DOI: DOI:

Alekszejenk´o et al | SUMO User Conference 2023 [9] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communicationefficient learning of deep networks from decentralized data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, A. Singh and J. Zhu, Eds.,ser. Proceedings of Machine Learning Research, vol. 54, PMLR, Apr. 2017, pp. 1273–1282. [Online]. Available:

J. Posner, L. Tseng, M. Aloqaily, and Y. Jararweh, “Federated learning in vehicular networks: Opportunities and solutions,” IEEE Network, vol. 35, no. 2, pp. 152–159, 2021. DOI: DOI:

A. Blanco-Justicia, J. Domingo-Ferrer, S. Mart´ınez, D. S´anchez, A. Flanagan, and K. E. Tan, “Achieving security and privacy in federated learning systems: Survey, research challenges and future directions,” Engineering Applications of Artificial Intelligence, vol. 106, p. 104 468, 2021, ISSN: 0952-1976. DOI: DOI:

H. Jiang, J. Li, P. Zhao, F. Zeng, Z. Xiao, and A. Iyengar, “Location privacy-preserving mechanisms in location-based services: A comprehensive survey,” ACM Comput. Surv.,vol. 54, no. 1, Jan. 2021, ISSN: 0360-0300. DOI: DOI:

K. Fukushima, “Cognitron: A self-organizing multilayered neural network,” Biological Cybernetics, vol. 20, pp. 121–136, 1975. DOI: DOI:

T. Tieleman and G. Hinton, “Lecture 6e – rmsprop: Divide the gradient by a running average of its recent magnitude,” Neural Networks for Machine Learning, 2012. [Online]. Available:

N. Kheterpal, K. Parvate, C. Wu, A. Kreidieh, E. Vinitsky, and A. Bayen, “Flow: Deep reinforcement learning for control in SUMO,” in SUMO 2018 – Simulating Autonomous and Intermodal Transport Systems, E. Wießner, L. L¨ucken, R. Hilbrich, et al., Eds., ser. EPiC Series in Engineering, vol. 2, EasyChair, 2018, pp. 134–151. DOI:




How to Cite

Alekszejenkó, L., & Dobrowiecki, T. (2023). SUMO Simulations for Federated Learning in Communicating Autonomous Vehicles: A Survey on Efficiency and Security. SUMO Conference Proceedings, 4, 115–129.

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