Privacy-Preserving Continuous User Authentication Using Federated Learning

In today’s increasingly digital landscape, continuous user authentication on smartphones has become crucial for safeguarding sensitive information. Behavioral biometrics, particularly facial recognition, is emerging as a powerful tool to enhance security, leveraging advanced machine learning and deep learning models. However, traditional approaches often involve sharing personal data for training, raising significant privacy concerns. Federated Learning (FL) addresses this issue by enabling decentralized model training directly on users’ devices, thus preserving privacy. Despite its promise, FL faces unique challenges in continuous user authentication, particularly due to the non-IID (non-Independent and Identically Distributed) nature of the data where every client has access only to one label data samples. While Convolutional Neural Networks (CNNs) are commonly employed in facial recognition, they struggle with the complexities of localized features and data distribution variance. This article explores all the possible architectures in order to tackle the CNNs weaknesses, we leverage the Vision Transformers (ViTs) and MLP-Mixers as a promising alternative to CNNs in the context of facial recognition. ViTs and MLP-Mixers excel in capturing global context and hierarchical representations, making them better suited to handle the complexities of continuous user authentication in FL. Through a case study, we demonstrate how integrating ViTs and MLP-Mixers into FL frameworks for facial recognition can enhance prediction accuracy and reduce weight divergence, offering a more robust and secure solution compared to other models.