Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization  

作　　者：Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang 

机构地区：[1]Henan Key Laboratory of Big Data Analysis and Processing,Computer and Information Engineering,Henan University,Kaifeng,475004,China [2]College of Information Science and Technology,Nanjing Forestry University,Nanjing,210037,China [3]Henan Branch,China Life Insurance Co.,Ltd.,Zhengzhou,450000,China

出　　处：《Computers, Materials & Continua》2025年第3期5035-5055,共21页计算机、材料和连续体(英文)

基　　金：supported by the National Natural Science Foundation of China(Nos.62002100,61902237);Key Research and Promotion Projects of Henan Province(Nos.232102240023,232102210063,222102210040).

摘　　要：Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关 键 词：Fingerprint recognition privacy protection federated learning adaptive weight adjustment 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP309[自动化与计算机技术—计算机科学与技术]