  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.
