mlsanityData to 7 Oct 2026

Federated Learning in Vehicular Networks

arXiv 2006.01412 ↗First posted 2 Jun 2020eess.SP cs.IT cs.LGcited by 53

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Abstract (arXiv)

Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data transmission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2020.

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Citing papers per year: 2021: 6; 2022: 14; 2023: 13; 2024: 10; 2025: 6; 2026: 4.

53 papers.

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  1. 2022Federated Learning via Inexact ADMMcited by 44
  2. 2023A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Taskscited by 41
  3. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  4. 2022Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Surveycited by 27
  5. 2023Federated Learning for Computer Visioncited by 22
  6. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  7. 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
  8. 2024Text-Enhanced Data-free Approach for Federated Class-Incremental Learningcited by 16
  9. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  10. 2023FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systemscited by 14
  11. 2022Deep Learning Serves Traffic Safety Analysis: A Forward-looking Reviewcited by 13
  12. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
38 more
  1. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  2. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  3. 2021Concept Drift Detection in Federated Networked Systemscited by 10
  4. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clusteringcited by 9
  5. 2021A Hybrid Architecture for Federated and Centralized Learningcited by 8
  6. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
  7. 2022FedGiA: An Efficient Hybrid Algorithm for Federated Learningcited by 8
  8. 2022Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITScited by 7
  9. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  10. 2023Federated Online and Bandit Convex Optimizationcited by 7
  11. 2024Hierarchical Federated Learning in Multi-hop Cluster-Based VANETscited by 6
  12. 2022Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sourcescited by 5
  13. 2024(FL)2: Overcoming Few Labels in Federated Semi-Supervised Learningcited by 5
  14. 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
  15. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  16. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  17. 2022Federated clustering with GAN-based data synthesiscited by 3
  18. 2024Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memorycited by 3
  19. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  20. 2023Data-Heterogeneous Hierarchical Federated Learning with Mobilitycited by 2
  21. 2024FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clientscited by 2
  22. 2025Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MRcited by 2
  23. 2022Exact Penalty Method for Federated Learningcited by 1
  24. 2023Federated Learning on Edge Sensing Devices: A Reviewcited by 1
  25. 2024Causal Influence in Federated Edge Inferencecited by 1
  26. 2024Buffer-based Gradient Projection for Continual Federated Learningcited by 1
  27. 2025Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learningcited by 1
  28. 2026Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applicationscited by 1
  29. 2022Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signalscited by 0
  30. 2022Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networkscited by 0
  31. 2022Detection and Prevention Against Poisoning Attacks in Federated Learningcited by 0
  32. 2023Intelligent Client Selection for Federated Learning using Cellular Automatacited by 0
  33. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  34. 2024FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributionscited by 0
  35. 2025Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluationcited by 0
  36. 2025Privacy-Preserving Offloading for Large Language Models in 6G Vehicular Networkscited by 0
  37. 2025Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networkscited by 0
  38. 2025Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networkscited by 0

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