FedRDA: Hierarchical Noise Detection for Federated Finger Vein Recognition

Abstract

Finger vein recognition offers significant advantages in biometric authentication, while federated learning addresses data silo challenges in distributed environments. However, label noise issues severely impact recognition performance due to variations in data acquisition environments, fluctuations in user registration quality, and privacy constraints preventing centralized annotation review. Existing label noise research typically focuses on sample-level processing, overlooking quality variations between authentication systems and noise distribution characteristics across multiple source devices. This paper proposes FedRDA, a federated optimization framework that achieves precise identification and adaptive correction of noisy samples through a three-tier progressive mechanism. We first construct a hierarchical noise detection system that identifies label noise from both noisy client and noisy sample perspectives. Then, we design a dynamic pseudo-label learning module with an improved adaptive label ambiguation loss function that dynamically adjusts sample learning difficulty parameters and incorporates momentum update mechanisms, significantly enhancing model adaptability to label noise of varying difficulty, while integrating predictive uncertainty entropy with unsupervised consistency constraints for more accurate label correction. Finally, we propose an adaptive aggregation strategy based on distance awareness and gradient consistency metrics to address data isolation and label noise issues in distributed environments. Experiments on SDUMLA, MMCBNU_6000, FV-USM, and combined datasets demonstrate that FedRDA maintains high model accuracy even under high noise rate conditions, with approximately 14% accuracy improvement over existing methods. The proposed framework effectively mitigates the negative impact of label noise on model training, ensuring robust operation of finger vein recognition systems in practical distributed environments while protecting user privacy.