Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning
Researchers propose Analytic Continual Unlearning (ACU), a gradient-free method enabling efficient removal of specific knowledge from pre-trained models during continuous learning phases while preserving privacy. The approach uses closed-form solutions to handle sequential forgetting requests, addressing gaps in existing unlearning techniques that struggle with privacy violations and adversarial request patterns.