In recent years， federated learning has become a new way to solve the problems of data island and privacy leakage in machine learning. Federated learning architecture does not require multiple parties to share data resources， in which participants only needed to train local models on local data and periodically upload parameters to the server to update the global model， and then a machine learning model can be built on large-scale global data. Federated learning architecture has the privacy-preserving nature and is a new scheme for large-scale data machine learning in the future. However， the parameter interaction mode of this architecture may lead to data privacy disclosure. At present， strengthening the privacy-preserving mechanism in federated learning architecture has become a new research hotspot. Starting from the privacy disclosure problem in federated learning， the attack models and sensitive information disclosure paths in federated learning were discussed， and several types of privacy-preserving techniques in federated learning were highlighted and reviewed， such as privacy-preserving technology based on differential privacy， privacy-preserving technology based on homomorphic encryption， and privacy-preserving technology based on Secure Multiparty Computation （SMC）. Finally， the key issues of privacy protection in federated learning were discussed， the future research directions were prospected.