The advent of Federated Learning (FL) highlights the practical necessity for the 'right to be forgotten' for all clients, allowing them to request data deletion from the machine learning model's service provider. This necessity has spurred a growing demand for Federated Unlearning (FU). Feature unlearning has gained considerable attention due to its applications in unlearning sensitive features, backdoor features, and bias features. Existing methods employ the influence function to achieve feature unlearning, which is impractical for FL as it necessitates the participation of other clients in the unlearning process. Furthermore, current research lacks an evaluation of the effectiveness of feature unlearning. To address these limitations, we define feature sensitivity in the evaluation of feature unlearning according to Lipschitz continuity. This metric characterizes the rate of change or sensitivity of the model output to perturbations in the input feature. We then propose an effective federated feature unlearning framework called Ferrari, which minimizes feature sensitivity. Extensive experimental results and theoretical analysis demonstrate the effectiveness of Ferrari across various feature unlearning scenarios, including sensitive, backdoor, and biased features.

翻译：联邦学习（FL）的出现凸显了所有客户端对“被遗忘权”的实际需求，允许他们向机器学习模型的服务提供商请求数据删除。这一需求催生了日益增长的联邦遗忘（FU）需求。特征遗忘因其在遗忘敏感特征、后门特征和偏见特征方面的应用而受到广泛关注。现有方法采用影响函数来实现特征遗忘，但这在联邦学习中不切实际，因为它需要其他客户端参与遗忘过程。此外，当前研究缺乏对特征遗忘有效性的评估。为应对这些局限，我们根据Lipschitz连续性定义了特征遗忘评估中的特征敏感度。该指标刻画了模型输出对输入特征扰动的变化率或敏感度。随后，我们提出了一种名为Ferrari的有效联邦特征遗忘框架，该框架通过最小化特征敏感度实现遗忘。大量实验结果和理论分析证明了Ferrari在多种特征遗忘场景（包括敏感特征、后门特征和偏见特征）中的有效性。