Federated Learning （FL） can effectively protect users' personal data from attackers. Differential Privacy （DP） is applied to enhance the privacy of FL， which can solve the problem of privacy disclose caused by parameters in the model training. However， existing FL methods based on DP on concentrate on the unified privacy protection budget and ignore the personalized privacy requirements of users. To solve this problem， a two-stage Federated Learning with Personalized Differential Privacy （PDP-FL） algorithm was proposed. In the first stage， the user's privacy was graded according to the user's privacy preference， and the noise meeting the user's privacy preference was added to achieve the purpose of personalized privacy protection. At the same time， the privacy level corresponding to the privacy preference was uploaded to the central aggregation server. In the second stage， in order to fully protect the global data， the simultaneous local and central protection strategy was adopted. And according to the privacy level uploaded by the user， the noise conforming to the global DP threshold was added to quantify the global privacy protection level. Experimental results show that on MNIST and CIFAR-10 datasets， the classification accuracy of PDP-FL algorithm reaches 93.8% to 94.5% and 43.4% to 45.2% respectively， which is better than those of Federated learning with Local Differential Privacy （LDP-Fed） algorithm and Federated Learning with Global Differential Privacy （GDP-FL） algorithm， PDP-FL algorithm meets the needs of personalized privacy protection.