A Federated Learning Approach for Privacy-Preserving Automated Signature Verification

The growing interconnectivity of digital systems has led to the massive collection and centralization of sensitive data, raising serious concerns about confidentiality and compliance with privacy regulations. Biometric authentication systems, such as offline signature verification, are particularly vulnerable. Federated Learning (FL) provides a promising framework by enabling model training without exposing raw client data. However, data scarcity remains a significant barrier to building robust Deep Learning (DL) models in such settings. This work investigates privacy-preserving Writer-Dependent (WD) Offline Signature Verification (OSV) within an FL framework. To address limited biometric datasets, we explore complementary techniques such as data augmentation, transfer learning, knowledge distillation, and meta-learning. These methods are integrated into federated training pipelines to enhance model generalization while preserving data confidentiality. Preliminary experiments suggest that combining FL with data scarcity mitigation strategies improves the robustness of signature verification systems. Augmentation and transfer learning, in particular, reduce overfitting and enhance classification performance, while knowledge distillation enables the use of lighter yet accurate models suitable for distributed environments. FL offers a viable pathway for secure and effective biometric authentication by keeping sensitive data local. Incorporating advanced data efficiency strategies further strengthens the reliability of offline signature verification systems. The proposed WD-OSV system was trained and evaluated on the popular CEDAR signature dataset, from which an average Area Under Curve (AUC) of 88.93%, along with an average binary accuracy (ACC) of 80.12% were reported as preliminary results. Beyond biometrics, the findings are extendable to healthcare applications, where privacy and data scarcity pose parallel challenges.