Abstract: Federated learning deploys deep learning training tasks on mobile edge networks. Mobile devices participating in learning only need to send the trained local models to the server instead of sending personal data, thereby protecting the data privacy of users. To speed up the implementation of federated learning, optimization of efficiency is the key. The main factors affecting efficiency include communication consumption between device and server, model convergence rate, and security and privacy risk of mobile edge networks. Based on thoroughly investigating the existing optimization methods, we summarize the efficiency optimization of federated learning into communication optimization, training optimization, and protection mechanism for the first time. Specifically, we discuss the optimization of federated learning communication from two aspects of edge computing coordination and model compression which can reduce the frequency of communication and resource consumption. Then, we review the optimization of federated learning process from four elements of device selection, resource coordination, model aggregation control, and data optimization similarly, because there are many heterogeneous factors in the mobile edge networks, such as the different computing resources of mobile devices and different data quality. Furthermore, the security and privacy protection mechanisms of federated learning are expounded. After comparing the innovation points and contributions of related technologies, the advantages and disadvantages of the existing solutions are concluded and the new challenges faced by federated learning are discussed. Finally, we propose edge-intelligent federated learning based on the idea of edge computing, provide innovative methods and future research directions in data optimization, adaptive learning, incentive mechanisms, and advanced technology.