With privacy legislation empowering the users with the right to be forgotten, it has become essential to make a model amenable for forgetting some of its training data. However, existing unlearning methods in the machine learning context can not be directly applied in the context of distributed settings like federated learning due to the differences in learning protocol and the presence of multiple actors. In this paper, we tackle the problem of federated unlearning for the case of erasing a client by removing the influence of their entire local data from the trained global model. To erase a client, we propose to first perform local unlearning at the client to be erased, and then use the locally unlearned model as the initialization to run very few rounds of federated learning between the server and the remaining clients to obtain the unlearned global model. We empirically evaluate our unlearning method by employing multiple performance measures on three datasets, and demonstrate that our unlearning method achieves comparable performance as the gold standard unlearning method of federated retraining from scratch, while being significantly efficient. Unlike prior works, our unlearning method neither requires global access to the data used for training nor the history of the parameter updates to be stored by the server or any of the clients.

翻译：随着隐私法规赋予用户“被遗忘权”，使模型能够遗忘部分训练数据变得至关重要。然而，由于学习协议的不同以及多方参与者的存在，机器学习环境中现有的遗忘方法无法直接应用于联邦学习等分布式场景。本文针对联邦学习中擦除客户端的问题，旨在消除受擦除客户端的全部本地数据对训练后全局模型的影响。为了实现客户端擦除，我们首先在待擦除客户端执行本地遗忘学习，随后以该本地遗忘模型为初始化，在服务器与剩余客户端之间运行极少量轮次的联邦学习，从而获得遗忘后的全局模型。我们通过三项数据集上的多项性能指标对遗忘方法进行实证评估，结果表明：与从头开始联邦重训练这一黄金标准遗忘方法相比，本方法在保持可比性能的同时显著提升了效率。与现有研究不同，本方法既无需全局访问训练数据，也无需服务器或任何客户端存储参数更新的历史记录。