Exploration on the Application of Federated Learning and Its Privacy Protection in Medical Scenarios
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 pa-per 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 ca-pability 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 general-ization and safety,need to be further studied.
federated learningprivacy protectionmedical research
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