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FedLVR: a federated learning-based fine-grained vehicle recognition scheme in intelligent traffic system

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Abstract

Fine-grained vehicle recognition has been widely used in intelligent transportation systems, such as urban traffic monitoring, traffic flow statistics and public security criminal investigation. Nevertheless, state-of-the-art fine-grained vehicle recognition systems usually employ a centralized deep learning model, which leads to costly computing resource consumption. In addition, amounts of vehicle image data are collected by a central server, where the involved sensitive information may be leaked. To address the concerns, we propose a privacy-preserving and lightweight federated learning-based framework for fine-grained vehicle recognition in edge-assisted intelligent traffic system, termed as FedLVR. Technically, we introduce a lightweight fine-grained vehicle recognition model that can be efficiently deployed on edge nodes, and develop its extensions based on federated learning framework. In particular, our model improves ShuffleNet v2 by designing parallel dual attention mechanism and introduces improved contrastive center loss, which highly enhances the feature discriminative ability of the model. Moreover, we propose a new weighted federated averaging algorithm to improve the generalization ability of the model. To clarify performance of our FedLVR, we conduct extensive experiments on the Stanford Cars dataset and Compcars dataset. The results show that our proposed FedLVR not only achieves high accuracy, but also greatly reduces the complexity of the network model. Compared to state-of-the-art research, our FedLVR effectively reduces the risk of privacy leakage and addresses the problem of data islands.

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Funding

This work was supported by National Natural Science Foundation of China (U1936213), Shanghai Rising-Star Program (No. 22QA1403800), Program of Shanghai Academic Research Leader (No.21XD1421500), and the Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province.

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Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 201306, China

    Jianqiu Zeng, Kai Zhang, Liangliang Wang & Jinguo Li

  2. Key Laboratory of Cryptography of Zhejiang Province, Hangzhou Normal University, Hangzhou, 311121, China

    Liangliang Wang

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  1. Jianqiu Zeng
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Correspondence to Kai Zhang.

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Zeng, J., Zhang, K., Wang, L. et al. FedLVR: a federated learning-based fine-grained vehicle recognition scheme in intelligent traffic system. Multimed Tools Appl 82, 37431–37452 (2023). https://doi.org/10.1007/s11042-023-15004-w

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