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（MU）的目标是为从训练过程中移除特定数据点的贡献提供理论保证。联邦遗忘（FU）则将MU扩展到从联邦训练流程中遗忘特定客户端的贡献。当前的FU方法通常不可扩展，且缺乏对遗忘效果的理论量化。本文提出了一种新型高效且可量化的联邦遗忘方法——知情联邦遗忘（IFU）。当接收到某客户端的遗忘请求时，IFU可识别联邦学习（FL）需重新初始化的最优迭代轮次，并通过随机扰动机制确保遗忘保证。本文还将IFU理论扩展到处理顺序遗忘请求的场景。在不同任务和数据集上的实验结果表明，与基础重训练及当前最优的FU方法相比，IFU能够实现更高效的遗忘流程。