1-D CNN-Based Online Signature Verification with Federated Learning
<|reference_start|>1-D CNN-Based Online Signature Verification with Federated Learning: Online signature verification plays a pivotal role in security infrastructures. However, conventional online signature verification models pose significant risks to data privacy, especially during training processes. To mitigate these concerns, we propose a novel federated learning framework that leverages 1-D Convolutional Neural Networks (CNN) for online signature verification. Furthermore, our experiments demonstrate the effectiveness of our framework regarding 1-D CNN and federated learning. Particularly, the experiment results highlight that our framework 1) minimizes local computational resources; 2) enhances transfer effects with substantial initialization data; 3) presents remarkable scalability. The centralized 1-D CNN model achieves an Equal Error Rate (EER) of 3.33% and an accuracy of 96.25%. Meanwhile, configurations with 2, 5, and 10 agents yield EERs of 5.42%, 5.83%, and 5.63%, along with accuracies of 95.21%, 94.17%, and 94.06%, respectively.<|reference_end|>
@article{zhang20241-d,
    title={1-D CNN-Based Online Signature Verification with Federated Learning},
    author={Lingfeng Zhang, Yuheng Guo, Yepeng Ding, Hiroyuki Sato},
    journal={arXiv preprint arXiv:2406.06597},
    year={2024},
    doi={10.1109/TrustCom60117.2023.00376},
    archivePrefix={arXiv},
    eprint={2406.06597},
    primaryClass={cs.CR cs.CV cs.LG}
}