Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA
Continual machine unlearning aims to remove the influence of data that should no longer be retained, while preserving the usefulness of the model on everything else. This setting becomes especially difficult when deletion requests arrive sequentially, because the model must repeatedly adapt without erasing previously retained knowledge. Low-Rank Adaptation (LoRA) offers an efficient way to implement such updates, but naively combining many sequential LoRA modules leads to parameter collision, causing \textit{strong interference} between tasks. We propose a static alternative based on Singular Value Decomposition (SVD)-guided orthogonal subspace projection. Our method constrains each new L...
| Authors | Yogachandran Rahulamathavan, Nasir Iqbal, Juncheng Hu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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