A Privacy-Preserving E-Voting System using Federated Learning and CNNs for Secure Fingerprint and Biometric Verification

The increasing reliance on electronic voting systems introduces challenges in securing voter data and ensuring privacy. Traditional e-voting systems are prone to cyber threats, compromising the integrity of elections, including data leakage, tampering, and spoofing attacks, as well as the problem of data centralization that increases the risk of hacking and loss of electoral integrity. To address this, we propose a secure e-voting system that uses federated learning to enhance security and privacy. The system employs multiple Convolutional Neural Networks (CNNs) including VGG16, VGG19, ResNet18, MobileNetV2, a custom CNN, and a ResNet-VGG hybrid across six distributed sites. In order to verify that only eligible and registered voters may cast ballots, the proposed system uses a three-step biometric process that includes user ID identification, fingerprint matching for high security using unique biometric data to prevent identity fraud, and gender identification as an extra layer of verification to lower the risk of identity theft. Furthermore, the system trains models locally and only transfer trained weights to avoid centralized sensitive data. When compared to the Sokoto-Coventry fingerprint dataset, it achieved high rates of 99.75% for identity recognition, 99.90% for fingerprint recognition, and 99.97% for gender recognition. These results highlight the effectiveness of the proposed system in providing a secure, privacy-preserving, and scalable solution for e-voting.