Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comply with the right to be forgotten, FL clients should be able to request the removal of their data contributions from the global model. In this paper, we propose FedQUIT, a novel unlearning algorithm that operates directly on client devices that request to remove its contribution. Our method leverages knowledge distillation to remove the influence of the target client's data from the global model while preserving its generalization ability. FedQUIT adopts a teacher-student framework, where a modified version of the current global model serves as a virtual teacher and the client's model acts as the student. The virtual teacher is obtained by manipulating the global model's outputs on forget data, penalizing the confidence assigned to the true class while preserving relationships among outputs of non-true classes, to simultaneously induce forgetting and retain useful knowledge. As a result, FedQUIT achieves unlearning without making any additional assumption over the standard FedAvg protocol. Evaluation across diverse datasets, data heterogeneity levels, and model architectures shows that FedQUIT achieves superior or comparable unlearning efficacy compared to six state-of-the-art methods, while significantly reducing cumulative communication and computational overhead relative to retraining from scratch.

翻译：联邦学习（FL）支持在不集中收集用户数据的情况下协作训练机器学习模型。为遵循“被遗忘权”，FL客户端应能请求从全局模型中删除其数据贡献。本文提出FedQUIT——一种直接在请求移除贡献的客户端设备上运行的新型遗忘算法。该方法利用知识蒸馏来消除目标客户端数据对全局模型的影响，同时保持模型的泛化能力。FedQUIT采用师生框架，将当前全局模型的修改版本作为虚拟教师，客户端模型作为学生。虚拟教师通过操控全局模型在遗忘数据上的输出获得：惩罚真实类别的置信度分配，同时保留非真实类别输出间的关联性，以同步实现遗忘与知识保留。因此，FedQUIT无需对标准FedAvg协议做任何附加假设即可实现遗忘。在多个数据集、数据异构程度及模型架构上的评估表明，与六种最先进方法相比，FedQUIT在遗忘效果上达到更优或相当水平，同时显著降低了累计通信与计算开销（相较于从头重新训练）。