  Federated learning (FL) is an emerging distributed machine learning paradigm
that protects privacy and tackles the problem of isolated data islands. At
present, there are two main communication strategies of FL: synchronous FL and
asynchronous FL. The advantages of synchronous FL are that the model has high
precision and fast convergence speed. However, this synchronous communication
strategy has the risk that the central server waits too long for the devices,
namely, the straggler effect which has a negative impact on some time-critical
applications. Asynchronous FL has a natural advantage in mitigating the
straggler effect, but there are threats of model quality degradation and server
crash. Therefore, we combine the advantages of these two strategies to propose
a clustered semi-asynchronous federated learning (CSAFL) framework. We evaluate
CSAFL based on four imbalanced federated datasets in a non-IID setting and
compare CSAFL to the baseline methods. The experimental results show that CSAFL
significantly improves test accuracy by more than +5% on the four datasets
compared to TA-FedAvg. In particular, CSAFL improves absolute test accuracy by
+34.4% on non-IID FEMNIST compared to TA-FedAvg.
