Federated learning is a distributed machine learning framework that effectively addresses the data silo problem and is crucial for ensuring privacy protection for individuals and organizations. However， enhancing the efficiency of federated learning remains a pressing issue due to the unsatisfactory high cost of federated learning caused by the characteristics of this learning. Therefore， a comprehensive summary and investigation of current mainstream research on improving the efficiency of federated learning was provided. Firstly， the background of efficient federated learning， including its origins and core ideas， was reviewed， and the concepts as well as classification of federated learning were explained. Secondly， the efficiency challenges generated by federated learning were discussed and categorized into heterogeneous problems， personalized problems， and communication cost issues. Thirdly， on the above basis， detailed solutions to these efficiency problems were analyzed and discussed， and the research on efficiency of federated learning was categorized into two areas： model compression optimization methods and communication optimization methods， and investigated. Fourthly， by comparison analysis， the advantages and disadvantages of each federated learning method were summarized， and the challenges still exist in efficient federated learning were expounded. Finally， the future research directions in efficient federated learning field were given.