bibtext: @InProceedings{ Den_Enable_MICCAI2024,
                   author = { Deng, Zhipeng and Luo, Luyang and Chen, Hao },
                   title = { { Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: The right to be forgotten, as stated in most data regulations, poses an underexplored challenge in federated learning (FL), leading to the development of federated unlearning (FU). However, current FU approaches often face trade-offs between efficiency, model performance, forgetting efficacy, and privacy preservation. In this paper, we delve into the paradigm of Federated Client Unlearning to guarantee a client the right to erase the contribution or the influence, introducing the first FU framework in medical imaging. In the unlearning process of a client, the proposed Model-Contrastive Unlearning marks a pioneering step towards feature-level unlearning, and Frequency-Guided Memory Preservation ensures smooth forgetting of local knowledge while maintaining the generalizability of the trained global model, thus avoiding performance compromises and guaranteeing rapid post-training. We evaluate our FCU framework on two public medical image datasets, including Intracranial hemorrhage diagnosis and skin lesion diagnosis, demonstrating our proposed framework outperforms other state-of-the-art FU frameworks working, with an expected speed-up of 10-15 times compared with retraining from scratch. The code and organized datasets will be made public.
title: Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
authors: Deng, Zhipeng Luo, Luyang Chen, Hao