With the development of trustworthy Federated Learning (FL), the requirement
of implementing right to be forgotten gives rise to the area of Federated
Unlearning (FU). Comparing to machine unlearning, a major challenge of FU lies
in the decentralized and privacy-preserving nature of FL, in which clients
jointly train a global model without sharing their raw data, making it
substantially more intricate to selectively unlearn specific information. In
that regard, many efforts have been made to tackle the challenges of FU and
have achieved significant progress. In this paper, we present a comprehensive
survey of FU. Specially, we provide the existing algorithms, objectives,
evaluation metrics, and identify some challenges of FU. By reviewing and
comparing some studies, we summarize them into a taxonomy for various schemes,
potential applications and future directions.

随着可信任的联邦学习（FL）的发展，实施被遗忘权引起了联邦遗忘（FU）领域，与机器遗忘相比，FU 的主要挑战在于 FL 的分散化和保护隐私的特性，其中客户端共同训练全局模型而不共享原始数据，使得有选择地遗忘特定信息变得更加复杂。针对此问题，我们对 FU 进行了全面的调查，提供了现有的算法、目标、评价指标，并确定了一些 FU 的挑战。通过回顾和比较一些研究，我们将它们总结为各种方案、潜在应用和未来方向的分类。