AI RESEARCH
Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
arXiv CS.LG
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ArXi:2604.26809v1 Announce Type: new 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.