In recent years, the notion of ``the right to be forgotten" (RTBF) has evolved into a fundamental element of data privacy regulations, affording individuals the ability to request the removal of their personal data from digital records. Consequently, given the extensive adoption of data-intensive machine learning (ML) algorithms and increasing concerns for personal data privacy protection, the concept of machine unlearning (MU) has gained considerable attention. MU empowers an ML model to selectively eliminate sensitive or personally identifiable information it acquired during the training process. Evolving from the foundational principles of MU, federated unlearning (FU) has emerged to confront the challenge of data erasure within the domain of federated learning (FL) settings. This empowers the FL model to unlearn an FL client or identifiable information pertaining to the client while preserving the integrity of the decentralized learning process. Nevertheless, unlike traditional MU, the distinctive attributes of federated learning introduce specific challenges for FU techniques. These challenges lead to the need for tailored design when designing FU algorithms. Therefore, this comprehensive survey delves into the techniques, methodologies, and recent advancements in federated unlearning. It provides an overview of fundamental concepts and principles, evaluates existing federated unlearning algorithms, reviews optimizations tailored to federated learning, engages in discussions regarding practical applications, along with an assessment of their limitations, and outlines promising directions for future research.

翻译：近年来，“被遗忘权”（RTBF）的概念已发展成为数据隐私法规的基本要素，赋予个人要求从数字记录中删除其个人数据的能力。因此，鉴于数据密集型机器学习算法的广泛采用以及个人数据隐私保护日益增长的重要性，机器遗忘的概念获得了广泛关注。机器遗忘使机器学习模型能够选择性地消除其在训练过程中获取的敏感或可识别个人信息。基于机器遗忘的基本原则，联邦遗忘应运而生，以应对联邦学习场景中的数据擦除挑战。这使联邦学习模型能够遗忘联邦学习客户端或与客户端相关的可识别信息，同时保持去中心化学习过程的完整性。然而，与传统的机器遗忘不同，联邦学习的独特属性给联邦遗忘技术带来了特定挑战。这些挑战要求在设计联邦遗忘算法时进行定制化设计。因此，这篇综合综述深入探讨了联邦遗忘的技术、方法论和最新进展。它概述了基本概念和原理，评估了现有的联邦遗忘算法，回顾了针对联邦学习的优化方法，讨论了实际应用及其局限性，并指出了未来有前景的研究方向。