Traditional machine learning (ML) algorithms need to collect a large mount of users' data for model training, which result in privacy leak and “data islands” problems emerge in endlessly. In order to solve the above problems, federated learning (FL) has emerged as an outstanding tool. FL is widely used for the sixth generation mobile network (6G) communications, artificial intelligence, and privacy-preserving applications. This article starts from the concept of FL, introduces the research status of FL algorithms and privacy-preserving technology, and further explains some of the current applications and future challenges. Although the FL has brought dawn, it still faces many challenges in terms of enhancing privacy-preserving and training model security. Communication overhead is a problem in the encryption process; the noise threshold of different scenarios needs to be solved in the process of noise handling; how to identify malicious attackers, and reduce malicious attacks is also a worth noticing challenge in modeling process.