Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
Open accessAbstract
Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning assisted connected autonomous vehicle (FLCAV) has been proposed, which leverages vehicular networks to establish federated deep neural networks (DNNs) from distributed data captured by vehicles and road sensors. Without the need of data aggregation, FLCAV helps preserve privacy while reducing communication costs compared with conventional centralized learning. However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios. This article presents networking and training frameworks for FLCAV perception. Multi-layer graph resource allocation and vehicle-road contrastive sensor placement are proposed to address the network management and sensor deployment problems, respectively. We also develop CarlaFLCAV, a software platform that implements the above system and methods. Experimental results confirm the superiority of the proposed techniques compared with various benchmarks.
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Studies have proposed techniques such as multistage resource allocation and strategic vehicle selection to optimize resource use and network bandwidth for FL in object detection and other applications.
“To reduce computation overhead and improve FL performance for object detection and other applications, studies have proposed various techniques such as multistage resource allocation and strategic vehicle selection to optimize resource use and network bandwidth [146].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [146]
The Introduction describes allocation of limited network resources across FL stages and tasks, and the section on CarlaFLCAV describes importance-aware vehicle selection under limited wireless capacity. The paper applies these methods to object detection and other perception tasks, but the cited passages address communication constraints and perception performance, not a demonstrated reduction in computation overhead. Page numbers are unavailable in our copy. (LLM, conf=0.83)
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- Authors
- Wang, S., Li, C., Ng, D. W. K., Eldar, Y. C., Poor, H. V., Hao, Q., & Xu, C.
- Affiliations
- Chinese Academy of Sciences (CN)
- The Hong Kong University of Science and Technology (Guangzhou) (CN)
- UNSW Sydney (AU)
- Weizmann Institute of Science (IL)
- Princeton University (US)
- Southern University of Science and Technology,China
- University of Macau (MO)
- Venue
- IEEE Network
- Published
- 2023-05
- Volume
- 37
- Issue
- 3
- Pages
- 16-25
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W4312678716
- Cited by
- 83
- References
- 15
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 26 · 2023: 24 · 2022: 4
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Network (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62001203)
- Intel (CG#52514373)
- Australian Research Council (DP210102169)
- 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
- federated learning, connected and autonomous vehicles, autonomous driving perception, vehicular networks, sensor placement, contrastive learning, multimodal dataset, privacy preservation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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