DpFedFKP: Dynamic personalized federated learning for finger knuckle print recognition

Finger knuckle print (FKP) recognition has recently attracted significant attention owing to its low cost, high security, and discriminative characteristics. However, existing data-driven approaches, particularly deep learning methods, typically require centralized training with large-scale datasets, raising serious privacy risks. Furthermore, the substantial data heterogeneity among independent datasets often leads to training difficulties and performance degradation. To address these challenges, we propose a dynamic personalized federated learning framework, called DpFedFKP, for FKP recognition. Specifically, each client first performs local training through classification loss constraints to obtain the local model, and the central server then aggregates these local models via Federated Averaging (FedAvg) to generate a generalized global model. Subsequently, each client performs an adaptive gradient analysis that computes gradients and parameter variations between global and local models using domain-specific sub-datasets. Furthermore, we propose a novel server-side dynamic update mechanism that dynamically adjusts the local model ratios within client-specific personalized models, enabling optimal interpolation between the global and local models to achieve robust generalization and personalization. Comprehensive experiments on two widely used FKP datasets demonstrate that the proposed method has achieved significant performance improvement over the state-of-the-art techniques. Specifically, our method achieves 0.03% relative accuracy improvement on Data-fkp and 0.52% relative accuracy improvement on PolyU-fkp, with 12.35% relative reduction in equal error rate (EER) on PolyU-fkp. Furthermore, the cross-client experiments achieve up to 2.80% relative accuracy improvement and 33.48% relative EER reduction. Significantly, the proposed DpFedFKP has strong compatibility with differential privacy techniques, thereby enhancing privacy-preserving capability.