Decentralized Federated Learning Links for Biometric Recognition

Jian Guo, Hengyu Mu, Hengyi Ren, Chong Han, Lijuan Sun

Abstract: In recent years, the recognition accuracies of deep learning-based biometric recognition methods, which rely on large amounts of biometric data for training, have significantly increased. However, in practical applications, biometric data are often distributed in small and fragmented amounts among various local clients. Implementing distributed biometric recognition is therefore greatly important. Most existing distributed biometric methods are implemented by federated learning and have achieved great success. However, the conversion from traditional local learning to distributed learning with multiterminal cooperation poses a series of security hazards, such as Byzantine attacks, inference attacks, etc, that have not been addressed. To address the issues, in this paper, a decentralized federated learning links (FedLink) for distributed biometric recognition is proposed, which is resistant to malicious attacks such as Byzantine attacks. Additionally, we validate the performance and security of FedLink by using two biometric traits, fingerprint and finger vein, on the NUPT-FPV dataset. The experimental results demonstrate that the FedLinks has excellent recognition accuracy and performs comparably to the unattacked model when subjected to various degrees of Byzantine attacks.

External IDs:dblp:conf/ijcnn/GuoMRHS24