Abstract: In recent years, artificial intelligence technology has played an increasingly important role in the dispatching control and data analysis of smart grids, but the problems of "data barriers" and privacy leakage in smart grid data analysis are urgent problems that need to be solved. Aiming at this, this paper introduces the emerging concept of federated learning in the field of artificial intelligence, analyzes the research status of federated learning in power data analysis, and explores the application scenarios of federated learning in power data analysis. Federated learning methods can maximize model accuracy while ensuring convergence and excellent performance of machine learning algorithms. Combining federated learning with power data analysis can not only maximize the role of stakeholders, but also meet the privacy protection needs of various stakeholders. Federated learning will open up a whole new path for the informatization and intelligent development of smart grids.