FedWiper: Federated Unlearning via Universal Adapter

Shuai Zhao, Junying Zhang, Xindi Ma, Qi Jiang, Zhuo Ma, Sheng Gao, Zuobin Ying, Jianfeng Ma

Abstract: Privacy preservation are becoming increasingly significant in machine learning, with recent privacy regulations requiring the deletion of personal data and its impact on models. Although erasing data from storage is simple, removing the influence of data on models remains a challenge. Federated unlearning is an emerging paradigm that aims to forget the knowledge contributed by some specific data to the federated model. In this paper, we design a novel federated unlearning strategy, named FedWiper, which enables exact unlearning in federated learning by erasing specific data and its impact from the federated model. Specifically, based on the granularity of the dataset, we propose training multiple federated submodels to construct a federated unlearning framework, thereby narrowing the scope of the impact of wiped data. Furthermore, the proposed Uni-Adapter structure effectively mitigates the negative impact on model performance from diminishing the dataset scale, while also reducing communication cost. Rather than focusing solely on achieving indistinguishability unlearning of the model for classification task, we extend FedWiper to unlearning for multiple types of tasks and achieve the exact unlearning. Experiments demonstrate that FedWiper can not only accelerate federated unlearning, but also achieve exact unlearning across multiple types of tasks in federated learning while ensuring minimal loss of model performance. Our Code: https://github.com/grey1989/FedWiper.