Toward Efficient and Secure Object Detection With Sparse Federated Training Over Internet of Vehicles

Qian, Yuwen; Rao, Luping; Ma, Chuan; Wei, Kang; Ding, Ming; Shi, Long

doi:10.1109/tits.2024.3389212

IEEE Trans. Intell. Transport. Syst.

2024

DOI: 10.1109/tits.2024.3389212

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Toward Efficient and Secure Object Detection With Sparse Federated Training Over Internet of Vehicles

Yuwen Qian,

Luping Rao,

Chuan Ma

et al.

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2024

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Cited by 2 publications

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DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection

Song,

Du,

Song

et al. 2024

Applied Sciences

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With the increasing complexity of neural network models, the huge communication overhead in federated learning (FL) has become a significant issue. To mitigate resource consumption, incorporating pruning algorithms into federated learning has emerged as a promising approach. However, existing pruning algorithms exhibit high sensitivity to network architectures and typically require multiple sessions of retraining to identify optimal structures. The direct application of such strategies to FL would inevitably introduce an additional communication cost. To this end, we propose a novel communication-efficient federated learning framework, DualPFL (Dual Sparse Pruning Federated Learning), designed to address these issues by implementing dynamic sparse pruning and adaptive model aggregation strategies. The experimental results demonstrate that, compared to similar works, our framework can improve convergence speed by more than two times under non-IID data, achieving up to 84% accuracy on the CIFAR-10 dataset, 95% mean average precision (mAP) on the COCO dataset using YOLOv8, and 96% accuracy on the TT100K traffic sign datasets. These findings indicate that DualPFL facilitates secure and efficient collaborative computing in smart city applications.

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DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection

Song,

Du,

Song

et al. 2024

Applied Sciences

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Communication-Efficient and Private Federated Learning with Adaptive Sparsity-Based Pruning on Edge Computing

Song,

Du,

Song

et al. 2024

Electronics

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As data-driven deep learning (DL) has been applied in various scenarios, the privacy threats have become a widely recognized problem. To boost privacy protection in federated learning (FL), some methods adopt a one-shot differential privacy (DP) approach to obfuscate model updates, yet they do not take into account the dynamic balance between efficiency and privacy protection. To this end, we propose ASPFL—an efficient FL approach with adaptive sparsity-based pruning and differential privacy protection. We further propose the adaptive pruning mechanism by utilizing the Jensen-Shannon divergence as the metric to generate sparse matrices, which are then employed in the model updates. In addition, we introduce adaptive Gaussian noise by assessing the variation of sensitivity through post-pruning uploading. Extensive experiments validate that our proposed ASPFL boosts convergence speed by more than two times under non-IID data. Compared with existing DP-FL methods, ASPFL can maximally achieve over 82% accuracy on CIFAR-10, while the communication cost is greatly reduced by 40% under the same level of privacy protection.

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Toward Efficient and Secure Object Detection With Sparse Federated Training Over Internet of Vehicles

Cited by 2 publications

References 48 publications

DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection

DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection

Communication-Efficient and Private Federated Learning with Adaptive Sparsity-Based Pruning on Edge Computing

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