  With the increasing emphasis on user privacy protection, biometric
recognition based on federated learning have become the latest research
hotspot. However, traditional federated learning methods cannot be directly
applied to finger vein recognition, due to heterogeneity of data and open-set
verification. Therefore, only a few application cases have been proposed. And
these methods still have two drawbacks. (1) Uniform model results in poor
performance in some clients, as the finger vein data is highly heterogeneous
and non-Independently Identically Distributed (non-IID). (2) On individual
client, a large amount of time is underutilized, such as the time to wait for
returning model from server. To address those problems, this paper proposes a
Personalized and Asynchronous Federated Learning for Finger Vein Recognition
(PAFedFV) framework. PAFedFV designs personalized model aggregation method to
solve the heterogeneity among non-IID data. Meanwhile, it employs an
asynchronized training module for clients to utilize their waiting time.
Finally, extensive experiments on six finger vein datasets are conducted. Base
on these experiment results, the impact of non-IID finger vein data on
performance of federated learning are analyzed, and the superiority of PAFedFV
in accuracy and robustness are demonstrated.
