  Federated Learning (FL) is a privacy-protected machine learning paradigm that
allows model to be trained directly at the edge without uploading data. One of
the biggest challenges faced by FL in practical applications is the
heterogeneity of edge node data, which will slow down the convergence speed and
degrade the performance of the model. For the above problems, a representative
solution is to add additional constraints in the local training, such as
FedProx, FedCurv and FedCL. However, the above algorithms still have room for
improvement. We propose to use the aggregation of all models obtained in the
past as new constraint target to further improve the performance of such
algorithms. Experiments in various settings demonstrate that our method
significantly improves the convergence speed and performance of the model.
