A Survey on Federated Learning Technology.

Because of the effectiveness of federated learning in protecting privacy and breaking the "data silo" phenomenon, it has been widely studied and applied in recent years. Firstly, the definition, classification and structure of federated learning are expounded. Secondly, the principle and development of federated algorithm are summarized. Then, it analyzes the challenges faced by federated learning, such as privacy protection, security aggregation, heterogeneity, and traffic cost, and summarizes the status quo of federated optimization algorithm from these aspects. Finally, it analyzes the development and feasibility of federated learning through the application of various industries, and summarizes the prospect.