  In federated learning, the models can be trained synchronously or
asynchronously. Many research works have focused on developing an aggregation
method for the server to aggregate multiple local models into the global model
with improved performance. They ignore the heterogeneity of the training
workers, which causes the delay in the training of the local models, leading to
the obsolete information issue. In this paper, we design and develop Asyn2F, an
Asynchronous Federated learning Framework with bidirectional model aggregation.
By bidirectional model aggregation, Asyn2F, on one hand, allows the server to
asynchronously aggregate multiple local models and results in a new global
model. On the other hand, it allows the training workers to aggregate the new
version of the global model into the local model, which is being trained even
in the middle of a training epoch. We develop Asyn2F considering the practical
implementation requirements such as using cloud services for model storage and
message queuing protocols for communications. Extensive experiments with
different datasets show that the models trained by Asyn2F achieve higher
performance compared to the state-of-the-art techniques. The experiments also
demonstrate the effectiveness, practicality, and scalability of Asyn2F, making
it ready for deployment in real scenarios.
