Poster: Towards Realistic Federated Learning Evaluations for Connected and Automated Vehicles
Pages 244 - 246
Abstract
Federated learning (FL) is widely recognized as a valuable approach for Connected and Automated Vehicles (CAVs) because it facilitates collaborative model development across a multitude of vehicles in a decentralized manner. However, numerous studies on FL algorithms only assessed their performance through experiments conducted in simulated client-server configurations (e.g., where both server and clients run on the same machine) or simplified scenarios that do not account for client downtime. In this paper, we aim to conduct more realistic evaluations for CAV applications leveraging FL. We present a preliminary experimental study as well as offer insights into potential future directions.
References
[1]
Vishnu Pandi Chellapandi, Liangqi Yuan, Stanislaw H /. Zak, and Ziran Wang. 2023. A Survey of Federated Learning for Connected and Automated Vehicles. arXiv:2303.10677 [cs.LG]
[2]
Mingzhe Chen, Nir Shlezinger, H Vincent Poor, Yonina C Eldar, and Shuguang Cui. 2021. Communication-efficient federated learning. Proceedings of the National Academy of Sciences 118, 17 (2021).
[3]
Keval Doshi and Yasin Yilmaz. 2022. Federated learning-based driver activity recognition for edge devices. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3338--3346.
[4]
Hao Gao, Yongkang Liu, Emrah Akin Sisbot, Yashar Zeinali Farid, Kentaro Oguchi, and Zhu Han. 2023. Hierarchical Federated Learning with Mean Field Game Device Selection for Connected Vehicle Applications. In 2023 IEEE Intelligent Vehicles Symposium (IV). IEEE, 1--6.
[5]
Mu Han, Kai Xu, Shidian Ma, Aoxue Li, and Haobin Jiang. 2022. Federated learning-based trajectory prediction model with privacy preserving for intelligent vehicle. International Journal of Intelligent Systems 37, 12 (2022), 10861--10879.
[6]
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr. 2020. FedML: A Research Library and Benchmark for Federated Machine Learning. Advances in Neural Information Processing Systems, Best Paper Award at Federate Learning Workshop (2020).
[7]
Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, and Qiang Yang. 2020. Fedvision: An online visual object detection platform powered by federated learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 13172--13179.
[8]
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2023. Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv:1602.05629 [cs.LG]
[9]
Gaith Rjoub, Jamal Bentahar, and Omar Abdel Wahab. 2022. Explainable AI-based federated deep reinforcement learning for Trusted Autonomous Driving. In 2022 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 318--323.
[10]
Afaf Taik, Zoubeir Mlika, and Soumaya Cherkaoui. 2022. Clustered vehicular federated learning: Process and optimization. IEEE Transactions on Intelligent Transportation Systems 23, 12 (2022), 25371--25383.
[11]
Zhu Tianqing, Wei Zhou, Dayong Ye, Zishuo Cheng, and Jin Li. 2021. Resource allocation in IoT edge computing via concurrent federated reinforcement learning. IEEE Internet of Things Journal 9, 2 (2021), 1414--1426.
[12]
Shuai Wang, Yuncong Hong, Rui Wang, Qi Hao, Yik-Chung Wu, and Derrick Wing Kwan Ng. 2022. Edge federated learning via unit-modulus over-the-air computation. IEEE Transactions on Communications 70, 5 (2022), 3141--3156.
[13]
Siyu Wang, Fangfang Liu, and Hailun Xia. 2021. Content-based vehicle selection and resource allocation for federated learning in IoV. In 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 1--7.
[14]
Liangqi Yuan, Lu Su, and Ziran Wang. 2023. Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application. IEEE Internet of Things Journal (2023).
Recommendations
Overtaking Feasibility Prediction for Mixed Connected and Connectionless Vehicles
Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fosters ...
Energy impact of different penetrations of connected and automated vehicles: a preliminary assessment
IWCTS '16: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation SciencePrevious research reported in the literature has shown the benefits of traffic coordination to alleviate congestion, and reduce fuel consumption and emissions. However, there are still many remaining challenges that need to be addressed before a massive ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2023
405 pages
Copyright © 2023 Copyright held by the owner/author(s).
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 August 2024
Check for updates
Qualifiers
- Poster
Conference
SEC '23
Sponsor:
Acceptance Rates
Overall Acceptance Rate 40 of 100 submissions, 40%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 13Total Downloads
- Downloads (Last 12 months)13
- Downloads (Last 6 weeks)1
Reflects downloads up to 19 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in