Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users' data from trained global FL models. A straightforward FU method involves removing the unlearned users and subsequently retraining a new global FL model from scratch with all remaining users, a process that leads to considerable overhead. To enhance unlearning efficiency, a widely adopted strategy employs clustering, dividing FL users into clusters, with each cluster maintaining its own FL model. The final inference is then determined by aggregating the majority vote from the inferences of these sub-models. This method confines unlearning processes to individual clusters for removing a user, thereby enhancing unlearning efficiency by eliminating the need for participation from all remaining users. However, current clustering-based FU schemes mainly concentrate on refining clustering to boost unlearning efficiency but overlook the potential information leakage from FL users' gradients, a privacy concern that has been extensively studied. Typically, integrating secure aggregation (SecAgg) schemes within each cluster can facilitate a privacy-preserving FU. Nevertheless, crafting a clustering methodology that seamlessly incorporates SecAgg schemes is challenging, particularly in scenarios involving adversarial users and dynamic users. In this connection, we systematically explore the integration of SecAgg protocols within the most widely used federated unlearning scheme, which is based on clustering, to establish a privacy-preserving FU framework, aimed at ensuring privacy while effectively managing dynamic user participation. Comprehensive theoretical assessments and experimental results show that our proposed scheme achieves comparable unlearning effectiveness, alongside offering improved privacy protection and resilience in the face of varying user participation.

翻译：联邦遗忘（Federated Unlearning, FU）因其能够从训练好的全局联邦学习（Federated Learning, FL）模型中消除特定用户数据的影响而日益受到重视。一种直接的FU方法是移除待遗忘用户，然后仅使用所有剩余用户从头开始重新训练一个新的全局FL模型，但这一过程会带来显著的开销。为提高遗忘效率，一种广泛采用的策略是利用聚类方法，将FL用户划分为多个簇，每个簇维护其自身的FL模型。最终的推断结果则通过聚合这些子模型推断结果的多数投票来决定。该方法将遗忘过程限制在单个簇内以移除用户，从而无需所有剩余用户参与，提高了遗忘效率。然而，当前基于聚类的FU方案主要集中于优化聚类以提升遗忘效率，却忽视了FL用户梯度可能造成的信息泄露，这是一个已被广泛研究的隐私问题。通常，在每个簇内集成安全聚合（Secure Aggregation, SecAgg）方案可以实现隐私保护的FU。然而，设计一种能够无缝整合SecAgg方案的聚类方法具有挑战性，尤其是在涉及对抗性用户和动态用户的场景中。为此，我们系统地探索了在最广泛使用的基于聚类的联邦遗忘方案中集成SecAgg协议，以建立一个隐私保护的FU框架，旨在确保隐私的同时有效管理动态用户参与。全面的理论评估和实验结果表明，我们提出的方案在实现可比遗忘效果的同时，提供了更强的隐私保护能力以及对用户参与变化的鲁棒性。