The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. Current FU approaches are generally not scalable, and do not come with sound theoretical quantification of the effectiveness of unlearning. In this work we present Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU approach. Upon unlearning request from a given client, IFU identifies the optimal FL iteration from which FL has to be reinitialized, with unlearning guarantees obtained through a randomized perturbation mechanism. The theory of IFU is also extended to account for sequential unlearning requests. Experimental results on different tasks and dataset show that IFU leads to more efficient unlearning procedures as compared to basic re-training and state-of-the-art FU approaches.

翻译：机器遗忘（Machine Unlearning， MU）的目标是对训练过程中移除给定数据点贡献的过程提供理论保证。联邦遗忘学习（Federated Unlearning， FU）将MU扩展至从联邦训练流程中遗忘特定客户端的贡献。现有FU方法普遍缺乏可扩展性，且缺少对遗忘效果的理论量化验证。本文提出信息型联邦遗忘学习（Informed Federated Unlearning， IFU），一种新型高效且可量化的FU方法。当收到客户端遗忘请求时，IFU能够识别联邦学习（FL）需重新初始化的最优迭代轮次，并通过随机扰动机制实现遗忘的理论保障。本文进一步将IFU理论扩展至处理顺序遗忘请求场景。在不同任务与数据集上的实验结果表明，相比基础重训练方法及现有最优FU方案，IFU能实现更高效的遗忘流程。