mlsanityData to 7 Oct 2026

A Survey of Federated Learning for Connected and Automated Vehicles

arXiv 2303.10677 ↗First posted 19 Mar 2023cs.LG cs.ROcited by 17

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Abstract (arXiv)

Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely used in CAVs for crucial tasks like perception, motion planning, and motion control, where machine learning models in CAVs are solely trained using the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning (FL) is an effective solution for CAVs that enables a collaborative model development with multiple vehicles in a distributed learning framework. FL enables CAVs to learn from a wide range of driving environments and improve their overall performance while ensuring the privacy and security of local vehicle data. In this paper, we review the progress accomplished by researchers in applying FL to CAVs. A broader view of the various data modalities and algorithms that have been implemented on CAVs is provided. Specific applications of FL are reviewed in detail, and an analysis of the challenges and future scope of research are presented.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 38 of its 96 references that are in the map. First posted on arXiv 19 Mar 2023.

Builds on

38 of 96 references.

References · 38

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
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  8. 2016End to End Learning for Self-Driving Carscited by 1,000
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  10. 2018How To Backdoor Federated Learningcited by 745
  11. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  12. 2021Federated Learning Based on Dynamic Regularizationcited by 555
26 more
  1. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  2. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 401
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  4. 2021A Field Guide to Federated Optimizationcited by 312
  5. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  6. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  7. 2017Network Topology and Communication-Computation Tradeoffs in Decentralized Optimizationcited by 119
  8. 2020Canadian Adverse Driving Conditions Datasetcited by 86
  9. 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
  10. 2020Federated Learning in Vehicular Networkscited by 53
  11. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  12. 2022FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Drivingcited by 41
  13. 2020IDDA: a large-scale multi-domain dataset for autonomous drivingcited by 31
  14. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  15. 2022The 6th AI City Challengecited by 23
  16. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  17. 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
  18. 2022Clustered Vehicular Federated Learning: Process and Optimizationcited by 15
  19. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  20. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11
  21. 2022Communication-Efficient Federated Distillation with Active Data Samplingcited by 10
  22. 2021A Hybrid Architecture for Federated and Centralized Learningcited by 8
  23. 2021Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehiclescited by 7
  24. 2020Turn Signal Prediction: A Federated Learning Case Studycited by 5
  25. 2017BlockChain: A distributed solution to automotive security and privacycited by 4
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 4; 2025: 3; 2026: 1.

17 papers.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  3. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  4. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  5. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  6. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  7. 2023FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communicationcited by 4
  8. 2023Digital Ethics in Federated Learningcited by 2
  9. 2024On the Federated Learning Framework for Cooperative Perceptioncited by 2
  10. 2024FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysiscited by 1
  11. 2023Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalizationcited by 0
  12. 2023Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructurecited by 0
5 more
  1. 2024Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learningcited by 0
  2. 2025Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoTcited by 0
  3. 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Networkcited by 0
  4. 2025Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Datacited by 0
  5. 2026MPT: Missing Prototype Tracking via Barycentric Reconstruction in Vehicular Federated Learningcited by 0