mlsanityData to 14 Oct 2026

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

arXiv 2308.10407 ↗First posted 21 Aug 2023cs.LG cs.CR cs.DCcited by 40

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Abstract (arXiv)

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed.

Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 106 of its 270 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

106 of 270 references.

References · 106

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38 more
  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  2. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
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  5. 2016End to End Learning for Self-Driving Carscited by 1,009
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 9; 2025: 20; 2026: 7.

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  1. 2025RAD: Retrieval-Augmented Decision-Making of Meta-Actions with Vision-Language Models in Autonomous Drivingcited by 18
  2. 2023Vehicle-to-Everything Cooperative Perception for Autonomous Drivingcited by 15
  3. 2025Decentralized Low-Rank Fine-Tuning of Large Language Modelscited by 14
  4. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 11
  5. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  6. 2023FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communicationcited by 4
  7. 2024When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weathercited by 4
  8. 2024Combining Federated Learning and Control: A Surveycited by 3
  9. 2025Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challengescited by 3
  10. 2023Digital Ethics in Federated Learningcited by 2
  11. 2024FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysiscited by 2
  12. 2024On the Federated Learning Framework for Cooperative Perceptioncited by 2
28 more
  1. 2024Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directionscited by 2
  2. 2025Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environmentscited by 2
  3. 2024On the Conflict of Robustness and Learning in Collaborative Machine Learningcited by 1
  4. 2025Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defensescited by 1
  5. 2025Probabilistic Segmentation for Robust Field of View Estimationcited by 1
  6. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  7. 2024Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learningcited by 0
  8. 2024Client-Side Patching against Backdoor Attacks in Federated Learningcited by 0
  9. 2025Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoTcited by 0
  10. 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Surveycited by 0
  11. 2025Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Datacited by 0
  12. 2025Federated Learning on Stochastic Neural Networkscited by 0
  13. 2025Lightweight Temporal Transformer Decomposition for Federated Autonomous Drivingcited by 0
  14. 2025Privacy-Preserving Quantized Federated Learning with Diverse Precisioncited by 0
  15. 2025Privacy-Preserving Driver Drowsiness Detection with Spatial Self-Attention and Federated Learningcited by 0
  16. 2025Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluationcited by 0
  17. 2025FRIEREN: Federated Learning with Vision-Language Regularization for Segmentationcited by 0
  18. 2025CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classificationcited by 0
  19. 2025Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approachescited by 0
  20. 2025DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learningcited by 0
  21. 2025Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learningcited by 0
  22. 2026PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learningcited by 0
  23. 2026FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detectioncited by 0
  24. 2026FedQHD: Closed-Form Function-Space Federated Reinforcement Learningcited by 0
  25. 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0
  26. 2026BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learningcited by 0
  27. 2026MPT: Missing Prototype Tracking via Barycentric Reconstruction in Vehicular Federated Learningcited by 0
  28. 2026Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETscited by 0