  Federated Learning (FL) since proposed has been applied in many fields, such
as credit assessment, medical, etc. Because of the difference in the network or
computing resource, the clients may not update their gradients at the same time
that may take a lot of time to wait or idle. That's why Asynchronous Federated
Learning (AFL) method is needed. The main bottleneck in AFL is communication.
How to find a balance between the model performance and the communication cost
is a challenge in AFL. This paper proposed a novel AFL framework VAFL. And we
verified the performance of the algorithm through sufficient experiments. The
experiments show that VAFL can reduce the communication times about 51.02\%
with 48.23\% average communication compression rate and allow the model to be
converged faster. The code is available at
\url{https://github.com/RobAI-Lab/VAFL}
