Security issues of artificial intelligence attract many attention in many research fields and industries, such as face recognition, medical care, and client services. Federated learning is proposed by Google, which can prevent the leakage of data during the AI training because each enterprise only needs to exchange training parameters without data sharing. In this paper, we present a novel differentially private federated learning framework (DP-FL) for unbalanced data. In the cloud server, DP-FL framework considers the unbalanced data of different users to set different privacy budgets. In the user client, we design a novel differential private convolutional neural networks with adaptive gradient descent (DPAGD-CNN) algorithm to update each user’s training parameters. Experimental results on several real-world datasets demonstrate that the DF-FL framework can protect data privacy with higher accuracy than existing works.

中文翻译：

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DP-FL：针对不平衡数据的新型差异私有联合学习框架

人工智能的安全性问题在人脸识别，医疗和客户服务等许多研究领域和行业中引起了很多关注。Google提出了联合学习，它可以防止在AI培训期间数据泄漏，因为每个企业只需要交换培训参数而无需数据共享。在本文中，我们提出了一种针对不平衡数据的新型差分私有联合学习框架（DP-FL）。在云服务器中，DP-FL框架会考虑不同用户的不平衡数据来设置不同的隐私预算。在用户客户端中，我们设计了具有自适应梯度下降（DPAGD-CNN）算法的新型差分专用卷积神经网络，以更新每个用户的训练参数。