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.

翻译：随着可信联邦学习（Federated Learning, FL）的发展，实现被遗忘权的要求催生了联邦遗忘学习（Federated Unlearning, FU）领域。与机器遗忘相比，FU的主要挑战在于FL的去中心化和隐私保护特性——客户端在不共享原始数据的情况下联合训练全局模型，这使得选择性遗忘特定信息变得极为复杂。为此，研究者已付出诸多努力应对FU的挑战并取得显著进展。本文对FU进行了全面综述，特别梳理了现有算法、目标、评估指标，并指出了FU面临的一些挑战。通过对比分析相关研究，我们将其归纳为包含不同方案、潜在应用及未来方向的分类体系。