Perception: A Systematic Review of Federated Learning for Privacy-Preserving Detection of Mixed Road Users in Intelligent Transportation Systems (ITS)
DOI:
https://doi.org/10.62054/ijdm/0204.12Abstract
The increasing complexity of urban environments, characterised by diverse mix of vehicles, and vulnerable road users such as pedestrians, and cyclists, poses significant safety and efficiency challenges for modern transportation. Intelligent Transportation Systems (ITS) aim to mitigate these issues, but the existing centralised models for road user detection face critical limitations regarding data privacy, latency, and scalability. Federated Learning (FL) has emerged as a promising decentralized paradigm to overcome these challenges by training models collaboratively without sharing raw data. This review followed the systematic literature review (SLR) methodology, with a comprehensive search of academic and scholarly databases and systematically analysed the current state of research on the application of Federated Learning in ITS for detecting mixed road users. Also examined in this work is the evolution of intelligent transport systems from centralized to decentralized and edge-based detection architectures, analyses the principles and advantages of Federated Learning, and explore the key scholarly debates surrounding its implementation, including the trade-offs between privacy and performance, data heterogeneity, and the gap between simulation and real-world deployment. This review highlights significant research gaps in the application of Federated Learning specifically for the task detection and classifications of vulnerable road user (VRU) in a mixed-user transport setting. The potential of Federated learning to enhance road safety through timely alert mechanism was explored, and the recommendations included that future research should consider among other things the creation of federated learning frameworks that are able to detect and classify different road users in a mixed road setting.
References
Aalavanthar, A., Famila, S., Sundaramurthy, S., Cirillo, S., Solimando, G., & Polese, G. (2025). Multi-objective federated learning traffic prediction in vehicular network for intelligent transportation system. PeerJ Computer Science, 11. https://doi.org/10.7717/peerj-cs.2922
Abdelkader, G., Elgazzar, K., & Khamis, A. (2021). Connected vehicles: Technology review, state of the art, challenges and opportunities. Sensors, 21(22). https://doi.org/10.3390/s21227712
Albadawi, Y., Takruri, M., & Awad, M. (2022). A Review of Recent Developments in Driver Drowsiness Detection Systems. In Sensors (Vol. 22, Issue 5). MDPI. https://doi.org/10.3390/s22052069
Alohali, M. A., Aljebreen, M., Nemri, N., Allafi, R., Duhayyim, M. Al, Ibrahim Alsaid, M., Alneil, A. A., & Osman, A. E. (2023). Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images. Remote Sensing, 15(12). https://doi.org/10.3390/rs15123092
Alqubaysi, T., Asmari, A. F. Al, Alanazi, F., Almutairi, A., & Armghan, A. (2025). Federated Learning-Based Predictive Traffic Management Using a Contained Privacy-Preserving Scheme for Autonomous Vehicles. Sensors, 25(4). https://doi.org/10.3390/s25041116
Andriulo, F. C., Fiore, M., Mongiello, M., Traversa, E., & Zizzo, V. (2024). Edge Computing and Cloud Computing for Internet of Things: A Review. In Informatics (Vol. 11, Issue 4). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/informatics11040071
Avcı, İ., & Koca, M. (2024). Intelligent Transportation System Technologies, Challenges and Security. Applied Sciences (Switzerland), 14(11). https://doi.org/10.3390/app14114646
Ayachi, R., Said, Y., Afif, M., Alshammari, A., Hleili, M., & Ben Abdelali, A. (2025). Assessing YOLO models for real-time object detection in urban environments for advanced driver-assistance systems (ADAS). In Alexandria Engineering Journal (Vol. 123, pp. 530–549). Elsevier B.V. https://doi.org/10.1016/j.aej.2025.03.077
Barbieri, L., Savazzi, S., Brambilla, M., & Nicoli, M. (2022). Decentralized federated learning for extended sensing in 6G connected vehicles. Vehicular Communications, 33. https://doi.org/10.1016/j.vehcom.2021.100396
Cao, J., Pang, Y., Xie, J., Khan, F. S., & Shao, L. (2021). From Handcrafted to Deep Features for Pedestrian Detection: A Survey. https://doi.org/10.1109/TPAMI.2021.3076733
Carletti, V., Foggia, P., Mazzocca, C., Parrella, G., & Vento, M. (2025). SoK: Gradient Inversion Attacks in Federated Learning. https://www.usenix.org/conference/usenixsecurity25/presentation/carletti
Chellapandi, V. P., Yuan, L., Zak, S. H., & Wang, Z. (2023). A Survey of Federated Learning for Connected and Automated Vehicles. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2485–2492. https://doi.org/10.1109/ITSC57777.2023.10421974
Chen, T., Yan, J., Sun, Y., Zhou, S., Gündüz, D., & Niu, Z. (2024). Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks. http://arxiv.org/abs/2401.09656
Choi, M. J., Ku, D. G., & Lee, S. J. (2022). Integrated YOLO and CNN Algorithms for Evaluating Degree of Walkway Breakage. KSCE Journal of Civil Engineering, 26(8), 3570–3577. https://doi.org/10.1007/s12205-022-1017-1
Cicek, D., & Kantarci, B. (2023). Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues. In Sensors (Vol. 23, Issue 3). MDPI. https://doi.org/10.3390/s23031699
Dimitrievski, M., Van Hamme, D., Veelaert, P., & Philips, W. (2020). Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections. Sensors (Switzerland), 20(17), 1–40. https://doi.org/10.3390/s20174817
Djenouri, Y., Belbachir, A. N., Michalak, T., Belhadi, A., & Srivastava, G. (2024). Enhancing smart road safety with federated learning for Near Crash Detection to advance the development of the Internet of Vehicles. Engineering Applications of Artificial Intelligence, 133. https://doi.org/10.1016/j.engappai.2024.108350
Elassy, M., Al-Hattab, M., Takruri, M., & Badawi, S. (2024). Intelligent transportation systems for sustainable smart cities. In Transportation Engineering (Vol. 16). Elsevier Ltd. https://doi.org/10.1016/j.treng.2024.100252
Ezzeddini, L., Ktari, J., Frikha, T., Alsharabi, N., Alayba, A., Alzahrani, A. J., Jadi, A., Alkholidi, A., & Hamam, H. (2024). Analysis of the performance of Faster R-CNN and YOLOv8 in detecting fishing vessels and fishes in real time. PeerJ Computer Science, 10. https://doi.org/10.7717/PEERJ-CS.2033
Ficzere, D., Varga, P., Wippelhauser, A., Hejazi, H., Csernyava, O., Kovács, A., & Hegedűs, C. (2023). Large-Scale Cellular Vehicle-to-Everything Deployments Based on 5G—Critical Challenges, Solutions, and Vision towards 6G: A Survey. Sensors, 23(16). https://doi.org/10.3390/s23167031
Gong, T., Zhu, L., Yu, F. R., & Tang, T. (2023). Edge Intelligence in Intelligent Transportation Systems: A Survey. IEEE Transactions on Intelligent Transportation Systems, 24(9), 8919–8944. https://doi.org/10.1109/TITS.2023.3275741
Hallak, K., & Kem, O. (2025). Adaptive federated learning framework for predicting EV charging stations occupancy. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2025.04.007
Herich, D., & Vaščák, J. (2024). The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV. In Drones (Vol. 8, Issue 2). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/drones8020034
Huang, X., Huang, T., Gu, S., Zhao, S., & Zhang, G. (2024). Responsible Federated Learning in Smart Transportation: Outlooks and Challenges. http://arxiv.org/abs/2404.06777
Jagatheesaperumal, S. K., Bibri, S. E., Huang, J., Rajapandian, J., & Parthiban, B. (2024). Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety. Computational Urban Science, 4(1). https://doi.org/10.1007/s43762-024-00120-6
Jiang, J. C., Kantarci, B., Oktug, S., & Soyata, T. (2020). Federated learning in smart city sensing: Challenges and opportunities. In Sensors (Switzerland) (Vol. 20, Issue 21, pp. 1–29). MDPI AG. https://doi.org/10.3390/s20216230
Khatua, S., De, D., Maji, S., Maity, S., & Nielsen, I. E. (2024). A federated learning model for integrating sustainable routing with the Internet of Vehicular Things using genetic algorithm. Decision Analytics Journal, 11. https://doi.org/10.1016/j.dajour.2024.100486
Khayesi, M. (2020). Vulnerable Road Users or Vulnerable Transport Planning? Frontiers in Sustainable Cities, 2. https://doi.org/10.3389/frsc.2020.00025
Li, M., Pan, X., Liu, C., & Li, Z. (2025). Federated deep reinforcement learning-based urban traffic signal optimal control. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-91966-1
Li, Q., Yu, W., Xia, Y., & Pang, J. (2025). From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges. http://arxiv.org/abs/2503.07505
Li, Q., Zhou, W., & Zheng, X. (2024). Distributed Learning in Intelligent Transportation Systems: A Survey. Information (Switzerland), 15(9). https://doi.org/10.3390/info15090550
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
Liang, S., Jin, S., & Chen, Y. (2024). A Review of Edge Computing Technology and Its Applications in Power Systems. In Energies (Vol. 17, Issue 13). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/en17133230
Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020). Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach. IEEE Internet of Things Journal, 7(8), 7751–7763. https://doi.org/10.1109/JIOT.2020.2991401
Mármol Campos, E., Hernandez-Ramos, J. L., González Vidal, A., Baldini, G., & Skarmeta, A. (2024). Misbehavior detection in intelligent transportation systems based on federated learning. Internet of Things (Netherlands), 25. https://doi.org/10.1016/j.iot.2024.101127
Murat, A. A., & Kiran, M. S. (2025). A comprehensive review on YOLO versions for object detection. In Engineering Science and Technology, an International Journal (Vol. 70). Elsevier B.V. https://doi.org/10.1016/j.jestch.2025.102161
Mustapha, A. A., Saruchi, S. ‘Atifah, Supriyono, H., & Solihin, M. I. (2025). A Hybrid Deep Learning Model for Waste Detection and Classification Utilizing YOLOv8 and CNN †. Engineering Proceedings, 84(1). https://doi.org/10.3390/engproc2025084082
Nagappan, G., Maheswari, K. G., & Siva, C. (2024). Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning. Simulation Modelling Practice and Theory, 135. https://doi.org/10.1016/j.simpat.2024.102979
Nagy, R., Török, Á., & Pethő, Z. (2024). Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk. Applied Sciences (Switzerland), 14(13). https://doi.org/10.3390/app14135661
Ometov, A., Molua, O. L., Komarov, M., & Nurmi, J. (2022). A Survey of Security in Cloud, Edge, and Fog Computing. In Sensors (Vol. 22, Issue 3). MDPI. https://doi.org/10.3390/s22030927
Rahman, M. A., Mammeri, A., & Metari, S. (2025). Intelligent Infrastructure for Enhancing Vulnerable Road User Safety using Machine Vision Technologies. International Journal of Intelligent Transportation Systems Research, 23(2), 1179–1196. https://doi.org/10.1007/s13177-025-00507-7
Reddy, S., Pillay, N., & Singh, N. (2024). Comparative Evaluation of Convolutional Neural Network Object Detection Algorithms for Vehicle Detection. Journal of Imaging, 10(7). https://doi.org/10.3390/jimaging10070162
Roy, R., & Saha, P. (2020). Analysis of vehicle-type-specific headways on two-lane roads with mixed traffic. Transport, 35(6), 588–604. https://doi.org/10.3846/transport.2020.14136
Saha, S., & Ahmad, T. (2021). Federated Transfer Learning: concept and applications. http://arxiv.org/abs/2010.15561
Saqib, S. M., Iqbal, M., Mazhar, T., Shahzad, T., Ouahada, K., & Hamam, H. (2025). Effectiveness of Teachable Machine, mobile net, and YOLO for object detection: A comparative study on practical applications. Egyptian Informatics Journal, 30. https://doi.org/10.1016/j.eij.2025.100680
Shang, B., Li, Y., Amin, A. G., Kamga, C., & Wei, J. (2025). AI-Enhanced Sensing for Vulnerable Road User Safety at Signalized Intersections: A Survey. Procedia Computer Science, 265, 350–357. https://doi.org/10.1016/j.procs.2025.07.191
Shao, Z., Yang, K., Sun, P., Hu, Y., & Boukerche, A. (2024). The evolution of detection systems and their application for intelligent transportation systems: From solo to symphony. Computer Communications, 225, 96–119. https://doi.org/10.1016/j.comcom.2024.06.015
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
Silva, R. M., Azevedo, G. F., Berto, M. V. V., Rocha, J. R., Fidelis, E. C., Nogueira, M. V., Lisboa, P. H., & Almeida, T. A. (2025). Vulnerable road user detection and safety enhancement: A comprehensive survey. Expert Systems with Applications, 292. https://doi.org/10.1016/j.eswa.2025.128529
Teixeira, P., Sargento, S., Rito, P., Luis, M., & Castro, F. (2023). A Sensing, Communication and Computing Approach for Vulnerable Road Users Safety. IEEE Access, 11, 4914–4930. https://doi.org/10.1109/ACCESS.2023.3235863
Thakur, A., & Yousuf, S. (2023). A Review Intelligent Transport System. GIS SCIENCE JOURNAL, 10(5). https://www.researchgate.net/publication/371131269
Ullah, I., Deng, X., Pei, X., Mushtaq, H., Uzair, M., & Qayyum, S. (2025). A blockchain-based federated learning framework against poisoning attacks in the internet of vehicles. Computer Networks, 272. https://doi.org/10.1016/j.comnet.2025.111705
Walch, M., Schirrer, A., & Neubauer, M. (2025). Impact assessment of cooperative intelligent transport systems (C-ITS): a structured literature review. In European Transport Research Review (Vol. 17, Issue 1). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1186/s12544-024-00702-9
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2023). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513–535. https://doi.org/10.1007/s13042-022-01647-y
Wu, Y., Wu, L., & Cai, H. (2023). A deep learning approach to secure vehicle to road side unit communications in intelligent transportation system. Computers and Electrical Engineering, 105. https://doi.org/10.1016/j.compeleceng.2022.108542
Yurdem, B., Kuzlu, M., Gullu, M. K., Catak, F. O., & Tabassum, M. (2024). Federated learning: Overview, strategies, applications, tools and future directions. In Heliyon (Vol. 10, Issue 19). Elsevier Ltd. https://doi.org/10.1016/j.heliyon.2024.e38137
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216. https://doi.org/10.1016/j.knosys.2021.106775
Zhang, R., Mao, J., Wang, H., Li, B., Cheng, X., & Yang, L. (2024). A Survey on Federated Learning in Intelligent Transportation Systems. IEEE Transactions on Intelligent Vehicles, 1–17. https://doi.org/10.1109/tiv.2024.3446319
Zhang, R., Wang, H., Li, B., Cheng, X., & Yang, L. (2024). A Survey on Federated Learning in Intelligent Transportation Systems. http://arxiv.org/abs/2403.07444
Zhang, S., Li, J., Shi, L., Ding, M., Nguyen, D. C., Tan, W., Weng, J., & Han, Z. (2024). Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems. IEEE Transactions on Intelligent Transportation Systems, 25(5), 3259–3285. https://doi.org/10.1109/TITS.2023.3324962
Zhang, Y., Zhou, A., Zhao, F., & Wu, H. (2022). A Lightweight Vehicle-Pedestrian Detection Algorithm Based on Attention Mechanism in Traffic Scenarios. Sensors, 22(21). https://doi.org/10.3390/s22218480
Zhang, Z., Wei, C., Wu, G., & Barth, M. J. (2025). Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey. In Applied Sciences (Switzerland) (Vol. 15, Issue 7). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/app15073797
Zhou, S., Wang, L., Chen, L., Wang, Y., & Yuan, K. (2025). Group verifiable secure aggregate federated learning based on secret sharing. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-94478-0
Zhou, Z., Zhu, J., Yu, F., Li, X., Peng, X., Liu, T., & Han, B. (2024). Model Inversion Attacks: A Survey of Approaches and Countermeasures. http://arxiv.org/abs/2411.10023
Zia, Q., Zhu, S., Wang, H., Iqbal, Z., & Li, Y. (2025). Hierarchical federated transfer learning in digital twin-based vehicular networks. High-Confidence Computing, 5(4). https://doi.org/10.1016/j.hcc.2025.100303
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Chijiuka F. Akuma, Pradeep Hewage, Tighe Bren, Basil O. Nwozaku, Amarachi Eche (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are solely responsible for obtaining permission to reproduce any copyrighted material contained in the manuscript as submitted. Any instance of possible prior publication in any form must be disclosed at the time the manuscript is submitted and a
copy or link to the publication must be provided.
The Journal articles are open access and are distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs 4.0 IGO License, which permits use,
distribution, and reproduction in any medium, provided the original work is properly cited.
No modifications or commercial use of the articles are permitted.
