Design and evaluation of a privacy-preserving multi-level federated learning architecture for airport biometric check-in

• Proposal of three architectures for biometric airport check-in systems. • Comparisonofcentralized and federated architecture to remark privacy preserving issues. • Quantitative and Qualitative Assessment for privacy analysis. • Trade-off analysis between privacy and accuracy in biometric systems. • Federated Learning-based strategies for privacy preservation. The rapid adoption of automated airport check-in systems using facial recognition raises significant privacy concerns due to their reliance on centralized deep learning models that store and transmit biometric data from edge devices. While Federated Learning (FL) is a promising approach for privacy preservation, its effectiveness in biometric identification remains underexplored, particularly in real-world environments like airports. This study assesses the privacy implications of FL in facial recognition by comparing three architectures. A first centralized system, where biometric data is sent to a central server for model training and inference, posing significant privacy risks. The second is a one-level FL architecture, where biometric data remains on local devices, and only model updates are shared with a central aggregator. The third is a two-level FL architecture, introducing an additional aggregation layer among airlines to enhance model generalization while preserving privacy. To ensure a rigorous privacy preservation evaluation, we integrate both quantitative and qualitative metrics. For the quantitative assessment, we leverage the Privacy Meter Tool, which enables simulations of Membership Inference Attacks and the application of Differential Privacy as a mitigation technique. For the qualitative evaluation, we conduct a Data Protection Impact Assessment to analyze potential privacy risks from a regulatory perspective. Additionally, we assess model accuracy, computational efficiency, and communication overhead to determine FL’s feasibility in large-scale airport environments. Our results show that while FL significantly reduces privacy risks, the two-level FL approach introduces new vulnerabilities, such as model poisoning risks and privacy-utility trade-offs, requiring further mitigation strategies like DP.