Federated Unlearning

no code implementations • 27 Dec 2020 • Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, Jiangchuan Liu

In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL.

Data Poisoning Federated Learning