Synthetic Driving Conditions Data Generation Using Federated Generative Adversarial Networks
DOI:
https://doi.org/10.5753/jisa.2025.5170Keywords:
Federated Learning, Generative Adversarial Networks, Synthetic Data Generation, Driving ConditionsAbstract
Road safety remains a global challenge, especially in scenarios where behavioral and environmental factors heavily influence drivers' decision-making. Machine learning models play a crucial role in enhancing safety and informed decision-making by learning effective actions based on traffic conditions. However, training these models requires access to user data, which can compromise drivers' privacy and expose sensitive information. To address this issue, this study proposes a solution for generating synthetic driving condition data using a Federated Learning approach combined with Generative Adversarial Networks (GAN). This method enables model training across multiple federated learning clients, preserving data privacy by avoiding direct data sharing. By leveraging the Harmony dataset, similarity metrics such as Euclidean Distance and KL-Divergence were integrated into the GAN loss function to improve the quality of the generated synthetic data. The results demonstrate that the proposed approach successfully generates realistic driving condition data, supporting centralized model training while maintaining user privacy, showcasing its potential in privacy-conscious road safety applications.
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Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. DOI: 10.48550/arXiv.1701.07875.
Beitollahi, M., Bie, A., Hemati, S., Brunswic, L. M., Li, X., Chen, X., and Zhang, G. (2024). Parametric feature transfer: One-shot federated learning with foundation models. DOI: 10.48550/arxiv.2402.01862.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. DOI: 10.1117/1.2819119.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357. DOI: 10.1613/jair.953.
Dai, Y., Wang, C., and Xie, Y. (2023). Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety. Accident Analysis & Prevention, 183:106975. DOI: 10.1016/j.aap.2023.106975.
de Souza, A. M., Braun, T., Botega, L. C., Villas, L. A., and Loureiro, A. A. F. (2020). Safe and sound: Driver safety-aware vehicle re-routing based on spatiotemporal information. IEEE Transactions on Intelligent Transportation Systems, 21(9):3973-3989. DOI: 10.1109/TITS.2019.2958624.
de Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., and Villas, L. A. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4):1550147716683612. DOI: 10.1177/1550147716683612.
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. DOI: 10.1016/j.adhoc.2024.103462.
de Souza, A. M., Oliveira, H. F., Zhao, Z., Braun, T., Loureiro, A. A., and Villas, L. A. (2022). Enhancing sensing and decision-making of automated driving systems with multi-access edge computing and machine learning. IEEE Intelligent Transportation Systems Magazine, 14(1):44-56. DOI: 10.1109/MITS.2019.2953513.
Geyer, R. C., Klein, T., and Nabi, M. (2018). Differentially private federated learning: A client level perspective. DOI: 10.48550/arxiv.1712.07557.
Ghavamipour, A. R., Turkmen, F., Wang, R., and Liang, K. (2023). Federated synthetic data generation with stronger security guarantees. In Proceedings of the 28th ACM Symposium on Access Control Models and Technologies, SACMAT '23, page 31–42, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3589608.3593835.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial networks. DOI: 10.1145/3422622.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved training of wasserstein gans. DOI: 10.48550/arxiv.1704.00028.
Guo, W. and Jiang, P. (2024). Weakly supervised anomaly detection with privacy preservation under a bi-level federated learning framework. Expert Systems with Applications, 254:124450. DOI: 10.1016/j.eswa.2024.124450.
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R. G. L., Eichner, H., Rouayheb, S. E., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., Gruteser, M., Harchaoui, Z., He, C., He, L., Huo, Z., Hutchinson, B., Hsu, J., Jaggi, M., Javidi, T., Joshi, G., Khodak, M., Konečný, J., Korolova, A., Koushanfar, F., Koyejo, S., Lepoint, T., Liu, Y., Mittal, P., Mohri, M., Nock, R., Özgür, A., Pagh, R., Raykova, M., Qi, H., Ramage, D., Raskar, R., Song, D., Song, W., Stich, S. U., Sun, Z., Suresh, A. T., Tramèr, F., Vepakomma, P., Wang, J., Xiong, L., Xu, Z., Yang, Q., Yu, F. X., Yu, H., and Zhao, S. (2021). Advances and open problems in federated learning. DOI: 10.1561/9781680837896.
Khoa, T. A., Trac, N. D., Tinh, V. P., Nam, N. H., Dang, D. N. M., Son, H. H., and Lam, P. D. (2024). Safety is our friend: A federated learning framework toward driver’s state and behavior detection. IEEE Transactions on Computational Social Systems, 11(2):2340-2358. DOI: 10.1109/TCSS.2023.3273727.
Kingma, D. P. and Welling, M. (2022). Auto-encoding variational bayes. DOI: 10.48550/arXiv.1312.6114.
Konečný, J., McMahan, H. B., Ramage, D., and Richtárik, P. (2016). Federated optimization: Distributed machine learning for on-device intelligence.
Kullback, S. and Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79–86. DOI: 10.1214/aoms/1177729694.
Lecun, Y., Jackel, L., Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., and Vapnik, V. (1995). Learning algorithms for classification: A comparison on handwritten digit recognition, pages 261-276. World Scientific. Available at: [link].
Li, D. and Wang, J. (2019). Fedmd: Heterogenous federated learning via model distillation. DOI: 10.48550/arxiv.1910.03581.
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3):50–60. DOI: 10.1109/msp.2020.2975749.
Lin, J. (1991). Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory, 37(1):145–151. DOI: 10.1109/18.61115.
Lin, Y., Bilotti, E., Bastiaansen, C. W., and Peijs, T. (2020). Transparent semi-crystalline polymeric materials and their nanocomposites: A review. Polymer Engineering & Science, 60(10):2351-2376. DOI: 10.1002/pen.25489.
M., J. J., K., D., F., K., P., A., D., G., and K., P. (2024). Enhanced generalization performance in deep learning for monitoring driver distraction: A systematic review. Journal of Advanced Research in Applied Sciences and Engineering Technology, 48(1):137-151. DOI: 10.37934/araset.48.1.137151.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2023). Communication-efficient learning of deep networks from decentralized data. DOI: 10.48550/arxiv.1602.05629.
Miller, L., Navarro, P. J., and Rosique, F. (2025). Edgenet: An end-to-end deep neural network pretrained with synthetic data for a real-world autonomous driving application. Sensors, 25(1). DOI: 10.3390/s25010089.
Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3):1065–1076. DOI: 10.1214/aoms/1177704472.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016). Improved techniques for training gans. DOI: 10.48550/arxiv.1606.03498.
Schwarting, W., Alonso-Mora, J., and Rus, D. (2018). Planning and decision-making for autonomous vehicles. Annual Review of Control, Robotics, and Autonomous Systems, 1(1):187–210. DOI: 10.1146/annurev-control-060117-105157.
Shaker, B., Pietro Rosati Papini, G., Saveriano, M., and Liang, K.-Y. (2024). Generating synthetic vehicle data using decentralized generative adversarial networks. IEEE Access, 12:138076-138085. DOI: 10.1109/ACCESS.2024.3428369.
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, pages 1-14, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbrc.2023.499.
Tamayo-Urgilés, D., Sanchez-Gordon, S., Valdivieso Caraguay, î L., and Hernández-Álvarez, M. (2024). Gan-based generation of synthetic data for vehicle driving events. Applied Sciences, 14(20). DOI: 10.3390/app14209269.
Tavakoli, A., Kumar, S., Guo, X., Balali, V., Boukhechba, M., and Heydarian, A. (2021). Harmony: A human-centered multimodal driving study in the wild. IEEE Access, 9:23956-23978. DOI: 10.1109/ACCESS.2021.3056007.
Tavakoli, A., Kumar, S., Guo, X., Balali, V., Boukhechba, M., and Heydarian, A. (2023). Harmony. Available at: [link] Retrieved from osf.io/zextd.
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. In Advances in Neural Information Processing Systems. DOI: 10.48550/arxiv.1907.00503.
Zhang, C., Yu, S., Tian, Z., and Yu, J. J. Q. (2023). Generative adversarial networks: A survey on attack and defense perspective. ACM Comput. Surv., 56(4). DOI: 10.1145/3615336.
Zhao, H., Wang, Y., Bashford-Rogers, T., Donzella, V., and Debattista, K. (2024a). Exploring generative ai for sim2real in driving data synthesis. In 2024 IEEE Intelligent Vehicles Symposium (IV), pages 3071-3077. DOI: 10.1109/IV55156.2024.10588493.
Zhao, S., Liao, T., Fu, L., Chen, C., Bian, J., and Zheng, Z. (2024b). Data-free knowledge distillation via generator-free data generation for non-iid federated learning. Neural Networks, 179:106627. DOI: 10.1016/j.neunet.2024.106627.
Zhu, Z., Hong, J., and Zhou, J. (2021). Data-free knowledge distillation for heterogeneous federated learning. DOI: 10.48550/arxiv.2105.10056.
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