  Federated learning has made an important contribution to data
privacy-preserving. Many previous works are based on the assumption that the
data are independently identically distributed (IID). As a result, the model
performance on non-identically independently distributed (non-IID) data is
beyond expectation, which is the concrete situation. Some existing methods of
ensuring the model robustness on non-IID data, like the data-sharing strategy
or pretraining, may lead to privacy leaking. In addition, there exist some
participants who try to poison the model with low-quality data. In this paper,
a performance-based parameter return method for optimization is introduced, we
term it FederatedSmart (FedSmart). It optimizes different model for each client
through sharing global gradients, and it extracts the data from each client as
a local validation set, and the accuracy that model achieves in round t
determines the weights of the next round. The experiment results show that
FedSmart enables the participants to allocate a greater weight to the ones with
similar data distribution.
