FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication
文献类型：期刊论文
| 作者 | Feng-Zhao Lian1,2; Jun-Duan Huang1; Ji-Xin Liu3; Guang Chen1,2; Jun-Hong Zhao1; Wen-Xiong Kang1 |
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2023 |
| 卷号 | 20期号:5页码:683-696 |
| 关键词 | Finger vein, personalized federated learning, privacy protection, biometric, authentication |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-022-1341-4 |
| 英文摘要 | Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user's finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/56003]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China 2.GRG Banking Equipment Co. Ltd., Guangzhou 510663, China 3.School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China |
| 推荐引用方式 GB/T 7714 | Feng-Zhao Lian,Jun-Duan Huang,Ji-Xin Liu,et al. FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication[J]. Machine Intelligence Research,2023,20(5):683-696. |
| APA | Feng-Zhao Lian,Jun-Duan Huang,Ji-Xin Liu, Guang Chen,Jun-Hong Zhao,&Wen-Xiong Kang.(2023).FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication.Machine Intelligence Research,20(5),683-696. |
| MLA | Feng-Zhao Lian,et al."FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication".Machine Intelligence Research 20.5(2023):683-696. |
入库方式： OAI收割
来源：自动化研究所
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