关键词: 文本分类, 分布式计算, 联邦学习, 差分隐私, 隐私保护

Abstract: As a distributed machine learning framework, federated learning can complete model training without disclosing user data. However, recent attacks have shown that only keeping the locality of data in the training process can not provide sufficient privacy protection. Therefore, in order to address the privacy protection issues during federated learning training, this paper proposes a text classification model based on BERT. This model combines differential privacy (DP) and federated learning (FL) to ensure that the federated model training process is protected from inference attacks during the transfer of federated learning parameters. The final experiment shows that the proposed method can maintain high model accuracy while protecting privacy.

Key words: text classification, distributed computing, federated learning, differential privacy, privacy protection