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\textsc{Lethe}: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning

arXiv:2601.22601v3 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase knowledge from a global model. Existing studies commonly assume that federated collaboration terminates after unlearning, overlooking a deployment-realistic scenario where training continues on the remaining clients after deletion requests are fulfilled. In this work, we identify a critical failure mode, termed knowledge resurfacing, revealing that continued training on retained data alone can...

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Lethe: Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning

Announce Type: replace Abstract: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model. Existing studies commonly assume that the collaboration ends with the unlearning operation, overlooking the follow-up situation where federated training continues over the remaining data. We identify a critical failure mode, termed knowledge resurfacing, by revealing that continued training can re-activate unlearned knowledge and cause...

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