A Survey on Federated Learning in Intelligent Transportation Systems
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Abstract (arXiv)
The development of Intelligent Transportation System (ITS) has brought about comprehensive urban traffic information that not only provides convenience to urban residents in their daily lives but also enhances the efficiency of urban road usage, leading to a more harmonious and sustainable urban life. Typical scenarios in ITS mainly include traffic flow prediction, traffic target recognition, and vehicular edge computing. However, most current ITS applications rely on a centralized training approach where users upload source data to a cloud server with high computing power for management and centralized training. This approach has limitations such as poor real-time performance, data silos, and difficulty in guaranteeing data privacy. To address these limitations, federated learning (FL) has been proposed as a promising solution. In this paper, we present a comprehensive review of the application of FL in ITS, with a particular focus on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. For each scenario, we provide an in-depth analysis of its key characteristics, current challenges, and specific manners in which FL is leveraged. Moreover, we discuss the benefits that FL can offer as a potential solution to the limitations of the centralized training approach currently used in ITS applications.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2024.
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Citing papers per year: 2025: 7; 2026: 2.
9 papers.
- 2025FedHQ: Hybrid Runtime Quantization for Federated Learningcited by 2
- 2025A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applicationscited by 1
- 2025Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.0cited by 1
- 2025Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detectioncited by 1
- 2026Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVscited by 1
- 2025Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluationcited by 0
- 2025Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimizationcited by 0
- 2025AutoFed: Personalized Federated Traffic Prediction via Adaptive Promptcited by 0
- 2026GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Taskcited by 0
- 2026Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs20 May 2026
- 2026GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task25 Feb 2026
- 2025AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt31 Dec 2025
- 2025Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization9 Nov 2025
- 2025Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation2 Sep 2025
- 2025Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection11 Jul 2025
- 2025Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.010 Jul 2025
- 2025A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications11 Jun 2025
- 2025FedHQ: Hybrid Runtime Quantization for Federated Learning17 May 2025