Abstract: Federated learning is a hot topic in the field of privacy protection, and it has a problem that it is difficult to concentrate local model parameters and data leakage due to gradient updates. This paper proposes a federated ensemble algorithm. The proposal uses a 256-byte key to transfer different types of initialization models to various data sources and do the training, and uses different ensemble algorithms to integrate local model parameters to ensure the security of the data and the model, thus greatly improving the security of data and model. Simulation results show that, for small and medium data sets, the accuracy of the model obtained by the adaboost integration algorithm reaches 92.505%, and the variance is about 8.6×10-8. For large data sets, the accuracy of the model obtained by the stacking ensemble algorithm reaches 92.495%, and the variance is about 8.85×10-8. Compared with the traditional method of training the model with integrated data, the proposal ensures the accuracy while taking into account the data and the model safety. 

Key words: federated learning, ensemble algorithm, privacy protection, federated ensemble algorithm