With the rapid development of artificial intelligence， the risk of user privacy disclosure is becoming serious increasingly. Differential privacy is a key privacy protection technology， which prevents personal information leakage by introducing noise into data， while Federated Learning （FL） allows joint training of models without exchanging data to protect data security. In recent years， differential privacy technology and FL are used together to give full play of their respective advantages： differential privacy ensures privacy protection in the process of data use， while FL improves the generalization ability and efficiency of the model through distributed training. Aiming at the privacy security problem of FL， firstly， the latest research progress of FL based on differential privacy was summarized and compared systematically， including different differential privacy mechanisms， FL algorithms and application scenarios. Secondly， special attention was paid to application approaches of differential privacy in FL， including data aggregation， gradient descent， and model training， and the advantages and disadvantages of various technologies were analyzed. Finally， the existing challenges and development directions of this field were summarized in detail.