  Federated learning aims to learn a global model collaboratively while the
training data belongs to different clients and is not allowed to be exchanged.
However, the statistical heterogeneity challenge on non-IID data, such as class
imbalance in classification, will cause client drift and significantly reduce
the performance of the global model. This paper proposes a simple and effective
approach named FedShift which adds the shift on the classifier output during
the local training phase to alleviate the negative impact of class imbalance.
We theoretically prove that the classifier shift in FedShift can make the local
optimum consistent with the global optimum and ensure the convergence of the
algorithm. Moreover, our experiments indicate that FedShift significantly
outperforms the other state-of-the-art federated learning approaches on various
datasets regarding accuracy and communication efficiency.
