关键词: 联邦学习, 差分隐私, 数据聚合, 梯度下降, 模型训练

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

Key words: federal learning, differential privacy, data aggregation, gradient descent, model training