Research on privacy protection of multi source data based on improved gbdt federated ensemble method with different metrics

Federated learning is a hot topic in the field of multi-source data. It has the advantage that the data cannot be localized, and it has the problems that the parameters of the local model are difficult to integrate and the accuracy of the model is low. In this paper, an improved gradient boosting decision tree (GBDT) federated ensemble learning method is proposed, which takes the average gradient of similar samples and its own gradient as a new gradient to improve the accuracy of the local model. Different ensemble learning methods are used to integrate the parameters of the local model, thus improving the accuracy of the updated global model. The experimental results show that the accuracy of the global model trained by the method on magic-gamma-telescope datasets and eeg-eye-state datasets is compared with that of GBDT federated ensemble method and traditional multi-source data processing technology without finding similar samples. The accuracy of the model is improved by 0.6%, 0.45% and 0.27% and 0.08% respectively, which makes it possible to improve the security of data and model while improving the accuracy of the model.