Purpose/Significance To explore the application of federated learning to conduct clinical research, and to carry out large model training while protecting patients’ privacy data, so as to promote the development of medical research. Method/Process The paper introduces the federated learning technology framework, and analyzes its great potential and possible problems in the fields of medical imaging, disease prediction, personalized therapy, new drug development, etc. Result/Conclusion Federated learning provides the capability to collaborate without sharing data in medical big data analysis, and provides the possibility for cross-institutional collaboration. At present, the problems of federated learning in medical research, such as data heterogeneity, communication efficiency, model generalization and safety, need to be further studied.