mlsanityData to 7 Oct 2026

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

arXiv 2309.11039 ↗First posted 20 Sep 2023cs.LG cs.AI cs.DCcited by 15

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Abstract (arXiv)

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 50 of its 204 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

50 of 204 references.

References · 50

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  9. 2016Virtual Worlds as Proxy for Multi-Object Tracking Analysiscited by 340
  10. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  11. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  12. 2016Speed/accuracy trade-offs for modern convolutional object detectorscited by 198
38 more
  1. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 159
  2. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  3. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 94
  4. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  5. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
  6. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  7. 2018A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Drivingcited by 54
  8. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  9. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  10. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  11. 2018Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learningcited by 26
  12. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  13. 2021Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehiclescited by 24
  14. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
  15. 2020Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learningcited by 19
  16. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  17. 2020FedCD: Improving Performance in non-IID Federated Learningcited by 13
  18. 2022Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networkscited by 13
  19. 2022Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognitioncited by 12
  20. 2021Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Schedulingcited by 11
  21. 2021FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computingcited by 11
  22. 2021Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systemscited by 10
  23. 2019Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weathercited by 9
  24. 2018Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systemscited by 8
  25. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  26. 2021Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Schedulingcited by 7
  27. 2021A Blockchain based Federated Learning for Message Dissemination in Vehicular Networkscited by 7
  28. 2022Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Dronescited by 7
  29. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
  30. 2021Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systemscited by 6
  31. 2021Personalized Federated Learning of Driver Prediction Models for Autonomous Drivingcited by 6
  32. 2019NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehiclescited by 5
  33. 2020Semi-Federated Learningcited by 5
  34. 2022Mobility, Communication and Computation Aware Federated Learning for Internet of Vehiclescited by 5
  35. 2019A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Gridcited by 4
  36. 2021Pooling is not Favorable: Decentralize Mining Power of PoW Blockchain Using Age-of-Workcited by 3
  37. 2013Decentralized Charging of Plug-In Electric Vehicles with Distribution Feeder Overload Controlcited by 1
  38. 2020A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystemcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 7; 2025: 4; 2026: 4.

15 papers.

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  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  2. 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Drivingcited by 8
  3. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  4. 2024A survey on secure decentralized optimization and learningcited by 4
  5. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
  6. 2025Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisitioncited by 3
  7. 2024Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizationscited by 0
  8. 2024Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Networkcited by 0
  9. 2025Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecastingcited by 0
  10. 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Surveycited by 0
  11. 2025Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosiscited by 0
  12. 2026U-STS-LLM A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputationcited by 0
3 more
  1. 2026Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Citiescited by 0
  2. 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0
  3. 2026Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETscited by 0