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, keeping data strictly localized inherently creates severe data scarcity, which is a significant barrier to building robust deep learning (DL) models. This work investigates the feasibility of a privacy-preserving writer-dependent (WD) offline signature verification (OSV) system within an FL framework. To make local training viable under these constraints, we integrate complementary techniques into the federated pipeline: data augmentation is utilized to increase local sample diversity, while transfer learning provides robust pre-trained feature representations, drastically reducing the volume of data required for effective local fine-tuning. The proposed WD-OSV system was trained and evaluated on the popular CEDAR signature dataset, for which an average area under the curve of 0.8893, along with an average binary accuracy (ACC) of 80.12%, are reported as preliminary results.