Frequent gradient exchange and heterogeneous data distribution in federated learning can lead to serious privacy leakage risks. Traditional privacy-preserving strategies fail to meet the personalized privacy needs from different users and may cause a decrease in model accuracy and convergence difficulties. The symmetry of federated learning may lead to the insufficiency of contribution evaluation mechanisms in protecting the privacy of sensitive data holders. However, federated learning avoids the risk of privacy leakage caused by data centralization because the raw data is always stored on the local device during the training process, and only encrypted model parameters or gradient updates are exchanged. To address these issues, this paper proposes an adaptive personalized differential privacy federated learning scheme APDP-FL. First, we propose an adaptive noise addition method that scores each round of training based on the parameters generated during training and dynamically adjusts the noise level for the next round. This method adds larger noise scales in the early stages of training, consuming less privacy budget, and gradually reduces noise addition during training to accelerate model convergence. Second, we design a personalized privacy protection strategy that adds noise tailored to individual needs for participating clients based on their privacy preferences. This solves the problem of insufficient or excessive privacy protection for some participants due to identical privacy budget sets for all clients, achieving personalized privacy protection for clients. Finally, we conduct extensive experimental simulations, comparisons, and analyses on three real federated datasets, MNIST, FMNIST, and CIFAR-10, verifying the advantages of APDP-FL in terms of privacy protection, model accuracy, and convergence speed.