In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distributed data and computing resources cannot be directly aggregated or shared among different regions or organizations for data processing or machine learning tasks. Federated learning and data federation emerge as efficient approaches to exploit distributed data and computing resources, so as to train machine learning models and to collaboratively process data, while obeying the laws and regulations, and ensuring data security and data privacy. In this talk, we present a functional architecture of federated learning systems including PaddleFL. Then, we present our research works based on federated learning systems in Baidu.