The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Decentralized Federated Learning for Road User Classification in Enhanced V2X Networks
Open accessAbstract
Federated Learning (FL) techniques are emerging in the automotive context to support connected automated driving services. Yet, when applied to vehicular use cases, conventional centralized FL policies show some drawbacks in terms of latency and scalability. This paper focuses on decentralized FL solutions, which attempt to overcome such limitations, by introducing a distributed computing architecture: vehicles exchange the parameters of a shared Machine Learning (ML) model via V2V links, without the need of a central orchestrator. Sharing all ML parameters, however, might not be feasible when minimal V2X bandwidth usage is required or the model is highly complex (e.g., extremely deep networks) as in advanced scenarios for high levels of automation. We thus propose a modular decentralized FL solution and we discuss its application to road user classification in a cooperative vehicular sensing use case. The proposed FL solution performs the point cloud processing of Lidar sensor inputs using a PointNet compliant architecture. It enables the exchange of a subset of the model parameters, namely selected ML model layers, optimized for communication efficiency, convergence and accuracy. We use real sensor data extracted from a publicly available dataset to validate the method, focusing on non-uniform scenarios where sensor data are highly unbalanced across the connected vehicles. For all cases, FL is shown to outperform the ego-sensing approach with minimal bandwidth usage.
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“Additionally, study [10] proposed a decentralized FL framework for object classification in CAVs using LiDAR data.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [10]
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“PointNet model parameters are exchanged over V2V networks, reducing data centralization and preserving privacy.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [10]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R., Su, H., Mo, K., & Guibas, L. J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.16 · Cited by 10,029
Advances and Open Problems in Federated Learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.04977 · Cited by 7,857
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,202
A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563
Research works printed but not linked: 1
- 0, Study on enhancement of 3GPP Support for 5G V2X Services (Release 16), 3rd Generation Partnership Project (3GPP) Technical Specification (TS) 22 886 12 2018
Cited by
Sorted by citations
Chellapandi, V. P., Yuan, L., Brinton, C. G., Żak, S. H., & Wang, Z. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3332675 · Cited by 271
A Survey of Collaborative Machine Learning Using 5G Vehicular Communications
Balkus, S. V., Wang, H., Cornet, B., Mahabal, C., Ngo, H. T., & Fang, H. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3149714 · Cited by 119
Chen, X., Deng, Y., Ding, H., Qu, G., Zhang, H., Li, P., & Fang, Y. M. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3370169 · Cited by 113
Decentralized federated learning for extended sensing in 6G connected vehicles
Barbieri, L., Savazzi, S., Brambilla, M., & Nicoli, M. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100396 · Cited by 113
Self-citationA Layer Selection Optimizer for Communication-Efficient Decentralized Federated Deep Learning
Barbieri, L., Savazzi, S., & Nicoli, M. (2023) · IEEE Access · doi:10.1109/access.2023.3251571 · Cited by 25
Self-citationTan, G., Yuan, H., Hu, H., Zhou, S., & Zhang, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3385913 · Cited by 25
Saravanarajan, V. B., Sridhar, S., Balasubramanian, V. R., Prabagarane, N., Mertens, J. S., Cafiso, S. D., Galluccio, L., Morabito, G., & Pappalardo, G. (2024) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom61746.2024.10646275 · Cited by 15
Communication-efficient Distributed Learning in V2X Networks: Parameter Selection and Quantization
Barbieri, L., Savazzi, S., & Nicoli, M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001364 · Cited by 12
Self-citation6G: Vision, Applications, and Challenges
da Costa, D. B., Zhao, Q., Chafii, M., Bader, F., & Debbah, M. A. (2023) · Signals and communication technology · doi:10.1007/978-3-031-37920-8_2 · Cited by 12
GCN-Based Topology Design for Decentralized Federated Learning in IoV
Li, Y., Xie, Q., Wang, W., Zhou, X., & Li, K. (2022) · Asia-Pacific Network Operations and Management Symposium (APNOMS) · doi:10.23919/apnoms56106.2022.9919905 · Cited by 6
SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
Gül, B. C., Tziampazis, S., Jazdi, N., & Weyrich, M. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205776 · Cited by 5
An Interaction-Aware Vehicle Behavior Prediction for Connected Automated Vehicles
Azadani, M. N., & Boukerche, A. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9839048 · Cited by 5
Su, X., Huo, Y., Wang, X., Mao, J., Jing, T., & Fan, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3590096 · Cited by 4
Grønli, T., Lakhan, A. R., & Younas, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39764-6_2 · Cited by 4
Samp-DSGD: Link Reliability Sampling Based Federated Learning for Vehicular Networks
Lei, X., & Xu, H.-T. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3361666 · Cited by 4
Towards Efficient Decentralized Federated Learning: A Survey
Thabet, S. K. S., Soltani, B., Zhou, Y., Sheng, Q. Z., & Wen, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0814-0_14 · Cited by 4
Evaluation of sensors impact on information redundancy in cooperative perception system
Chawky, B. S., Hefeida, M., & Noureldin, A. M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001526 · Cited by 3
An Object Detection Method Based on Heterogeneous Lidar Point Clouds Using Federated Learning
Zhou, Y., Li, B., & Huang, K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1099-7_24 · Cited by 2
Lin, X., Ai, D., Ma, B., Wang, X., Yu, G., He, Y., Ni, W., & Liu, R. P. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-4522-7_17 · Cited by 2
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“Despite various advances, high computational costs and complexity associated with HE still persist when integrating in PPML [2].”
Fu, Y., Huang, Y., Chang, T., Cai, X., Li, C., & Cheng, N. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3619965 · Cited by 1
Joint Aggregation Node Selection and Power Adjustment for Federated Learning in IoV
Sang, Z., Meng, C., Yan, W., Gao, B., Xiong, K., & Fan, P. (2025) · International Conference on Computer, Control and Robotics (ICCCR) · doi:10.1109/icccr65461.2025.11072661 · Cited by 1
Decentralized Federated Learning for Internet of Vehicles With Asymmetric Networks
Ji, Q., Wen, G., & Fang, X. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3677043 · Cited by 1
A review of federated learning: architectures, challenges, and targeted solutions
Velaei, M., Ghahramani, H., Ghaffari, A., & Derakhshanfard, N. (2026) · Cluster Computing · doi:10.1007/s10586-026-06286-4 · Cited by 1
Cooperative Processing and Learning Methods for High-Resolution Environmental Perception
Barbieri, L. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-51500-2_10 · Cited by 1
Self-citationSpecial Topics in Information Technology
Amigoni, F. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-51500-2
Coordinating Autonomous Vehicles Using Reinforcement Learning
Jung, J., Shun, M., & Krishnamachari, B. (2025) · IEEE SoutheastCon · doi:10.1109/southeastcon56624.2025.10971480
Autonomous Model Aggregation for Decentralized Learning on Edge Devices
Chen, J., Tu, J., Yang, L., & Cao, J. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3621058
Hussain, M. I., Zahra, S. R., & Gupta, A. (2025) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn67655.2025.11368250
Comparative Analysis of AI-Driven Traffic Light Optimization
Yu, P., & Gao, Y. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2373-0.ch006
Kittikamron, K., Manop, N., Suppakitpaisarn, V., & Rojviboonchai, K. (2026) · IEEE Access · doi:10.1109/access.2026.3741414
Metadata
- Authors
- Barbieri, L., Savazzi, S., & Nicoli, M.
- Affiliations
- Politecnico di Milano (IT)
- National Research Council (IT)
- Venue
- 2021 IEEE International Conference on Communications Workshops (ICC Workshops)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 1-6
- ISSN
- 2164-7038 (ISSN-L)
- OpenAlex
- W3178207769
- Cited by
- 30
- References
- 19
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 9 · 2023: 4 · 2022: 5 · 2021: 1
- FWCI
- 2.19
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE International Conference on Communications workshops (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Funders
- Interface
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- decentralized federated learning, road user classification, vehicle-to-everything communication, LiDAR point cloud processing, PointNet, communication efficiency, bandwidth reduction
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