异步联邦反学习与不变性校准用于医学影像
Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
AI总结：
本文提出AFU-IC框架，通过解耦反学习过程与全局训练流程，实现异步反学习，同时利用服务器端不变性校准机制防止模型重新学习被删除的数据，实验表明其在医学影像中具有高效且合规的联邦学习能力。
摘要：
联邦反学习（FU）是联邦学习（FL）中新兴的范式，使参与客户端能够完全从训练好的全局模型中移除其贡献，受数据保护法规驱动。然而，现有FU方法大多依赖同步协调，迫使整个联邦暂停等待滞后者完成擦除，导致显著延迟。此外，这些方法常面临被删除数据的影响仅被暂时抑制并在后续训练中重新出现的问题。为克服这些限制，本文提出异步联邦反学习与不变性校准（AFU-IC），一种针对医学影像的新型框架，将擦除过程与全局训练工作流程解耦，使目标客户端可异步执行反学习而不中断全局训练。同时，服务器端不变性校准机制防止模型重新学习被删除的数据。在三个医学基准上的大量实验表明，AFU-IC在反学习效果和模型保真度上与黄金标准重训练相当，同时显著减少实际时间延迟，相较于同步基线。AFU-IC确保了跨站点医学环境中的高效、合规和可靠的联邦学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
