Privacy-aware continuous federated biometric authentication (PACFBA) using federated deep learning: a novel usable security framework.

With the rapid growth of mobile, IoT, and edge-based ecosystems, the need for secure, seamless, and privacy-preserving authentication mechanisms has become increasingly critical. Traditional authentication methods, such as passwords and single-step biometrics, verify users only at the initial login stage and fail to ensure continuous user verification throughout an active session, exposing systems to significant security risks. To address this limitation, this study proposes the Privacy-Aware Continuous Federated Biometric Authentication (PACFBA) framework, a novel Federated Learning (FL)-based approach for continuous, decentralized user authentication. PACFBA integrates a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to jointly capture spatial and temporal biometric patterns, improving adaptability in heterogeneous environments. To ensure secure model updates and protect user data, PACFBA incorporates Differential Privacy (DP) and Homomorphic Encryption (HE) within the FL process, preventing the transfer of sensitive biometric information to central servers. Experimental results demonstrate that PACFBA achieves an accuracy of 92.5%, precision of 90.8%, recall of 91.2%, and an F1-score of 0.91, while reducing communication overhead by 20% and improving privacy preservation by 35% compared to centralized models. These results confirm PACFBA's potential for enhancing continuous authentication in mobile, IoT, and edge-based environments. However, further empirical validation using large-scale, heterogeneous, real-world datasets is necessary before the framework can be considered fully deployment-ready. This research contributes to designing scalable, secure, and adaptive authentication frameworks to address emerging challenges in decentralized digital ecosystems.