Aiming at the problem that the improved federated average algorithm based on analytic hierarchy process was affected by subjective factors when calculating its data quality， an improved federated weighted average algorithm was proposed to process multi-source data from the perspective of data quality. Firstly， the training samples were divided into pre-training samples and pre-testing samples. Then， the accuracy of the initial global model on the pre-training data was used as the quality weight of the data source. Finally， the quality weight was introduced into the federated average algorithm to reupdate the weights in the global model. The simulation results show that the model trained by the improved federal weighted average algorithm get the higher accuracy compared with the model trained by the traditional federal average algorithm， which is improved by 1.59% and 1.24% respectively on equally divided and unequally divided datasets. At the same time， compared with the traditional multi-party data retraining method， although the accuracy of the proposed model is slightly reduced， the security of data and model is improved.