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

• URL: http://arxiv.org/abs/2601.22601v1
• Date: Fri, 30 Jan 2026 05:50:35 GMT
• Title: Lethe:Adapter-Augmented Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
• Authors: Hanwei Tan, Wentai Hu, Ligang He, Yijun Quan,
• Abstract summary: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model.<n>We identify a critical failure mode, termed Knowledge resurfacing, by revealing that continued training can re-activate unlearned knowledge.<n>We propose Lethe, a novel federated unlearning method that de-correlates knowledge to be unlearned from knowledge to be retained.
• Score: 6.171968410497911
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 up with the unlearning operation, overlooking the follow-up situation where the 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 the removed influence to resurface in the global model. To address this, we propose Lethe, a novel federated unlearning method that de-correlates knowledge to be unlearned from knowledge to be retained, ensuring persistent erasure during continued training.Lethe follows a Reshape--Rectify--Restore pipeline: a temporary adapter is first trained with gradient ascent on the unlearning data to obtain magnified updates, which is then used as corrective signals to diverge layer-wise rectification on the remaining updates in two streams. Finally, the adapter is removed and a short recovery stage is performed on the retained data. Our experiments show that Lethe supports unlearning in the federated system at all levels in a unified manner and maintains superior persistence (Resurfacing Rate <1% in most cases) even after numerous rounds of follow-up training.

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