In the problem of predicting disease scores amid the protection of multi-source domain data considering user privacy, the decentralized data from different source domains cannot be combined and may follow different distributions. Therefore, traditional machine learning methods cannot be applied directly to utilize the information within source domains. In this study, the federated importance weighting method is proposed combining the idea of federated learning and the sample-based transfer learning approach. By re-weighting the samples from the source domains to the prediction task of the target domain, and without data sharing between multiple source domains, it realizes the use of data with different distributions while protecting the data privacy of the source domains. Moreover, this study constructs a weighted model and provides a concise and general algorithm to solve the prediction model for the target domain. Numerical simulation and empirical results show that, compared with the traditional method without considering distribution shift, the federated importance weighting method can effectively utilize the information of the source domain data. It is superior in prediction accuracy of the target domain and can make an accurate prediction of disease scores in the Parkinson’s disease data.