In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de facto standard for privacy protection in federated learning due to its rigorous mathematical foundation and provable guarantee. Despite extensive research on algorithms that incorporate differential privacy within federated learning, there remains an evident deficiency in systematic reviews that categorize and synthesize these studies. Our work presents a systematic overview of the differentially private federated learning. Existing taxonomies have not adequately considered objects and level of privacy protection provided by differential privacy in federated learning. To rectify this gap, we propose a new taxonomy of differentially private federated learning based on definition and guarantee of differential privacy and federated scenarios. Our classification allows for a clear delineation of the protected objects across various differential privacy models and their respective neighborhood levels within federated learning environments. Furthermore, we explore the applications of differential privacy in federated learning scenarios. Our findings provide valuable insights into privacy-preserving federated learning and suggest practical directions for future research.

我们的研究针对差分隐私的联邦学习进行了系统的概述和分类，提出了一种新的基于差分隐私和联邦场景定义和保证的分类方法，并探讨了差分隐私在联邦学习场景中的应用，为隐私保护的联邦学习提供了有价值的洞见和未来研究方向。

差分隐私联邦学习：系统性综述