mlsanityData to 14 Oct 2026

Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles

arXiv 2102.03401 ↗First posted 5 Feb 2021eess.SY cs.AI cs.LGcited by 26

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Abstract (arXiv)

A new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs). In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in the FL training process and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, as well as the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal autonomous controller. In particular, the impacts of varying CAV participation in the FL process and diverse CAV data quality on the convergence of the proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an incentive mechanism based on contract theory is designed to improve the FL convergence speed. Simulation results using real vehicular data traces show that the proposed DFP-based controller can accurately track the target CAV speed over time and under different traffic scenarios. Moreover, the results show that the proposed DFP algorithm has a much faster convergence compared to popular FL algorithms such as federated averaging (FedAvg) and federated proximal (FedProx). The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 14 of its 44 references that are in the map. First posted on arXiv 5 Feb 2021.

Builds on

14 of 44 references.

References · 14

  1. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  2. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  4. 2016End to End Learning for Self-Driving Carscited by 1,009
  5. 2017Federated Multi-Task Learningcited by 672
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 254
  9. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  10. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  11. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  12. 2017End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependenciescited by 16
2 more
  1. 2020Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theorycited by 16
  2. 2018Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systemscited by 8

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 8; 2024: 11; 2025: 3.

26 papers.

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  1. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  2. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  3. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  4. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  5. 2021Faithful Edge Federated Learning: Scalability and Privacycited by 13
  6. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 10
  7. 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computingcited by 9
  8. 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailabilitycited by 9
  9. 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
  10. 2023Approximate and Weighted Data Reconstruction Attack in Federated Learningcited by 4
  11. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  12. 2024Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networkscited by 4
14 more
  1. 2024FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learningcited by 4
  2. 2023Collaborative Learning with a Drone Orchestratorcited by 3
  3. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  4. 2024Combining Federated Learning and Control: A Surveycited by 3
  5. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  6. 2024On the Convergence Rates of Federated Q-Learning across Heterogeneous Environmentscited by 2
  7. 2024PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scalecited by 1
  8. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  9. 2021Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)cited by 0
  10. 2023OASIS: Offsetting Active Reconstruction Attacks in Federated Learningcited by 0
  11. 2024Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Networkcited by 0
  12. 2024Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computingcited by 0
  13. 2025Degree of Staleness-Aware Data Updating in Federated Learningcited by 0
  14. 2025Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systemscited by 0