  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 various differential privacy models
in federated learning. To rectify this gap, we propose a new taxonomy of
differentially private federated learning based on definition and guarantee of
various differential privacy models 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 work provide valuable
insights into privacy-preserving federated learning and suggest practical
directions for future research.
