Abstract: Most existing fingerprint recognition methods rely on machine learning,which neglects the privacy and heterogeneity of the data when training on massive databases,resulting in user information leakage and reduced recognition accuracy.To cooperatively optimize model accuracy under privacy protection,this paper proposes a novel fingerprint recognition algorithm based on federated learning,termed federated learning-fingerprint recognition(Fed-FR).Firstly,the algorithm iteratively aggregates parameters from each terminal through federated learning,thereby improving the performance of the global model.Secondly,sparse representation theory is applied to low-quality fingerprint image denoising to enhance the texture structure of the fingerprint.Thirdly,in response to the allocation inequity issue caused by client heterogeneity,this paper proposes a client scheduling strategy based on reservoir sampling.Finally,experimental results on three real-world databases show that Fed-FR significantly outperforms local learning by 5.32% and federated average by 8.56%,approaching the accuracy of centralized learning.The results demonstrate the effectiveness of Fed-FR in privacy protection,accuracy evaluation,and scalability.This study demonstrates for the first time the feasibility of combining federated learning with fingerprint recognition,enhancing the security and scalability of fingerprint recognition algorithms,and providing a reference for the application of federated learning in biometric technologies.

Key words: Fingerprint recognition, Federated learning, Sparse representation, Reservoir sampling, Privacy protection