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

arXiv: https://arxiv.org/abs/2102.03401 · HTML: https://mlsanity.com/paper/2102.03401/

## 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 (most cited first, 14 of 14)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies](https://mlsanity.com/paper/1710.03804/) (2017)
- [Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory](https://mlsanity.com/paper/2003.04451/) (2020)
- [Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems](https://mlsanity.com/paper/1806.04589/) (2018)

## Built on by (most cited first, 26 of 26)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [A Survey of Federated Learning for Connected and Automated Vehicles](https://mlsanity.com/paper/2303.10677/) (2023)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Faithful Edge Federated Learning: Scalability and Privacy](https://mlsanity.com/paper/2106.15905/) (2021)
- [Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction](https://mlsanity.com/paper/2308.04237/) (2023)
- [Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2404.08444/) (2024)
- [Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability](https://mlsanity.com/paper/2409.17446/) (2024)
- [Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions](https://mlsanity.com/paper/2209.04022/) (2022)
- [Approximate and Weighted Data Reconstruction Attack in Federated Learning](https://mlsanity.com/paper/2308.06822/) (2023)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks](https://mlsanity.com/paper/2402.04216/) (2024)
- [FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning](https://mlsanity.com/paper/2409.12105/) (2024)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2405.18707/) (2024)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units](https://mlsanity.com/paper/2401.12862/) (2024)
- [On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments](https://mlsanity.com/paper/2409.03897/) (2024)
- [PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale](https://mlsanity.com/paper/2405.17839/) (2024)
- [Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning](https://mlsanity.com/paper/2504.09114/) (2025)
- [Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)](https://mlsanity.com/paper/2201.00626/) (2021)
- [OASIS: Offsetting Active Reconstruction Attacks in Federated Learning](https://mlsanity.com/paper/2311.13739/) (2023)
- [Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network](https://mlsanity.com/paper/2410.10451/) (2024)
- [Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing](https://mlsanity.com/paper/2411.01140/) (2024)
- [Degree of Staleness-Aware Data Updating in Federated Learning](https://mlsanity.com/paper/2508.16931/) (2025)
- [Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems](https://mlsanity.com/paper/2509.00992/) (2025)

## 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.