  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 (FCU) 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
evaluated our FCU framework on two public medical image datasets, including
Intracranial hemorrhage diagnosis and skin lesion diagnosis, demonstrating that
our framework outperformed other state-of-the-art FU frameworks, with an
expected speed-up of 10-15 times compared with retraining from scratch. The
code and the organized datasets can be found at:
https://github.com/dzp2095/FCU.
