The increasing concerns regarding the privacy of machine learning models have catalyzed the exploration of machine unlearning, i.e., a process that removes the influence of training data on machine learning models. This concern also arises in the realm of federated learning, prompting researchers to address the federated unlearning problem. However, federated unlearning remains challenging. Existing unlearning methods can be broadly categorized into two approaches, i.e., exact unlearning and approximate unlearning. Firstly, implementing exact unlearning, which typically relies on the partition-aggregation framework, in a distributed manner does not improve time efficiency theoretically. Secondly, existing federated (approximate) unlearning methods suffer from imprecise data influence estimation, significant computational burden, or both. To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation. Instead, we leverage new memories to overwrite old ones, imitating the process of \textit{active forgetting} in neurology. Specifically, the model, intended to unlearn, serves as a student model that continuously learns from randomly initiated teacher models. To preserve catastrophic forgetting of non-target data, we utilize elastic weight consolidation to elastically constrain weight change. Extensive experiments on three benchmark datasets demonstrate the efficiency and effectiveness of our proposed method. The result of backdoor attacks demonstrates that our proposed method achieves satisfying completeness.

翻译：机器学习模型日益增长的隐私担忧催生了机器遗忘的研究，即消除训练数据对机器学习模型影响的过程。这一担忧在联邦学习领域同样存在，促使研究者着手解决联邦遗忘问题。然而，联邦遗忘仍然具有挑战性。现有遗忘方法大致可分为两类：精确遗忘和近似遗忘。首先，以分布式方式实现精确遗忘（通常依赖于分区-聚合框架）在理论上并不能提升时间效率。其次，现有的联邦（近似）遗忘方法存在数据影响估计不精确、计算负担显著或两者兼具的问题。为此，我们提出一种基于增量学习的新型联邦遗忘框架，该框架不依赖于特定模型或联邦设置。我们的框架不同于依赖近似重训练或数据影响估计的现有联邦遗忘方法。相反，我们利用新记忆覆盖旧记忆，模仿神经学中的"主动遗忘"过程。具体而言，旨在执行遗忘的模型作为学生模型，持续从随机初始化的教师模型中学习。为避免非目标数据的灾难性遗忘，我们采用弹性权重巩固来弹性约束权重变化。在三个基准数据集上的广泛实验证明了我们提出方法的效率和有效性。后门攻击的结果表明，所提出的方法实现了令人满意的完整性。