Federated Unlearning (FU) is an emerging paradigm in Federated Learning (FL) that enables participating clients to fully remove their contributions from a trained global model, driven by data protection regulations that mandate the right to be forgotten. However, existing FU methods mostly rely on synchronous coordination. This requirement forces the entire federation to halt and wait for stragglers to complete erasure, creating significant delays due to device heterogeneity. Furthermore, these methods often face the problem that the influence of erased data is merely suppressed temporarily and resurfaces during subsequent training, rather than being genuinely removed. To overcome these limitations, this paper proposes Asynchronous Federated Unlearning with Invariance Calibration (AFU-IC), a novel framework for medical imaging that decouples the erasure process from the global training workflow. This enables the target client to perform unlearning asynchronously without interrupting global training. Meanwhile, a server-side invariance calibration mechanism prevents the model from relearning the erased data. Extensive experiments on three medical benchmarks demonstrate that AFU-IC achieves unlearning efficacy and model fidelity comparable to gold-standard retraining while significantly reducing wall-clock latency compared to synchronous baselines. AFU-IC ensures efficient, compliant and reliable FL in cross-silo medical environments.

翻译：联邦遗忘学习（FU）是联邦学习（FL）中的一种新兴范式，它使参与客户端能够从训练好的全局模型中完全移除其贡献，这是由“被遗忘权”的数据保护法规所驱动的。然而，现有的联邦遗忘学习方法大多依赖于同步协调。这一要求迫使整个联邦系统停止运行并等待落后者完成擦除操作，由于设备异质性，这会造成显著延迟。此外，这些方法常常面临一个问题：被擦除数据的影响仅仅被暂时抑制，并在后续训练中重新浮现，而非被真正移除。为克服这些局限，本文提出了一种面向医学影像的新型框架——基于不变性校准的异步联邦遗忘学习（AFU-IC）。该框架将擦除过程与全局训练工作流解耦，使得目标客户端能够异步执行遗忘操作，而无需中断全局训练。同时，服务器端的不变性校准机制阻止了模型重新学习已被擦除的数据。在三个医学基准数据集上的大量实验表明，AFU-IC 在遗忘效果和模型保真度方面与黄金标准的重训练方法相当，同时与同步基线方法相比，显著降低了挂钟延迟。AFU-IC 确保了跨孤岛医疗环境中 FL 的高效性、合规性和可靠性。