T. Zeng, O. Semiari, M. Chen, W. Saad and M. Bennis, "Federated Learning on the Road Autonomous Controller Design for Connected and Autonomous Vehicles," in IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 10407-10423, Dec. 2022, doi: 10.1109/TWC.2022.3183996
Federated learning on the road autonomous controller design for connected and autonomous vehicles
|Author:||Zeng, Tengchan1; Semiari, Omid2; Chen, Mingzhe3,4;|
1Ford Motor Company, Dearborn, MI 48124 USA
2Department of Electrical and Computer Engineering, University of Colorado, Colorado Springs, CO 80918 USA
3Department of Electrical Engineering, Princeton University, Princeton, NJ 08544 USA
4Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China
5Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203 USA
6Centre for Wireless Communications, University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 9.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301183455
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-18
The deployment of future intelligent transportation systems is contingent upon seamless and reliable operation of connected and autonomous vehicles (CAVs). One key challenge in developing CAVs is the design of an autonomous controller that can accurately execute near real-time control decisions, such as a quick acceleration when merging to a highway and frequent speed changes in a stop-and-go traffic. However, the use of conventional feedback controllers or traditional learning-based controllers, solely trained by each CAV’s local data, cannot guarantee a robust controller performance over a wide range of road conditions and traffic dynamics. In this paper, a new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of 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 non-independent 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.
IEEE transactions on wireless communications
|Pages:||10407 - 10423|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Office of Naval Research (ONR) under MURI Grant N00014-19-1-2621, in part by the U.S. National Science Foundation under Grant CNS-1739642 and Grant CNS-1941348, in part by the Academy of Finland Project CARMA, in part by the Academy of Finland Project MISSION, in part by the Academy of Finland Project SMARTER, and in part by the INFOTECH Project NOOR.
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