{"type":"ScholarlyArticle","title":"Fine-Grained and Efficient Self-Unlearning with Layered Iteration","authors":[{"name":"Hongyi Lyu","slug":"hongyi-lyu","given":"Hongyi","family":"Lyu","affiliation":""},{"name":"Xuyun Zhang","slug":"xuyun-zhang","given":"Xuyun","family":"Zhang","affiliation":""},{"name":"Hongsheng Hu","slug":"hongsheng-hu","given":"Hongsheng","family":"Hu","affiliation":""},{"name":"Shuo Wang","slug":"shuo-wang","given":"Shuo","family":"Wang","affiliation":""},{"name":"Chaoxiang He","slug":"chaoxiang-he","given":"Chaoxiang","family":"He","affiliation":""},{"name":"Lianyong Qi","slug":"lianyong-qi","given":"Lianyong","family":"Qi","affiliation":""}],"abstract":"As machine learning models become widely deployed in data-driven applications, ensuring compliance with the 'right to be forgotten' as required by many privacy regulations is vital for safeguarding user privacy. To forget the given data, existing re-labeling based unlearning methods employ a single-step adjustment scheme that revises the decision boundaries in one re-labeling phase. However, such single-step approaches lead to coarse-grained changes in decision boundaries among the remaining classes and impose adverse effects on the model utility. To address these limitations, we propose 'Self-Unlearning with Layered Iteration (SULI),' a novel unlearning approach that introduces a layered iteration strategy to re-label the forgetting data iteratively and refine the decision boundaries progressively. We further develop a 'Selective Probability Adjustment (SPA)' technique, which uses a soft-label mechanism to promote smoother decision-boundary transitions. Comprehensive experiments on three benchmark datasets demonstrate that SULI achieves superior performance in effectiveness, efficiency, and privacy compared to the state-of-the-art baselines in both class-wise and instance-wise unlearning scenarios. The source code is released at https://github.com/Hongyi-Lyu-MQ/SULI.","doi":"10.24963/ijcai.2025/850","url":"https://ijcai.org/proceedings/2025/850","pdf":"https://ijcai.org/proceedings/2025/0850.pdf","markdown":"https://ijcai.org/proceedings/2025/850.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/850","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":850,"pages":{"first":7643,"last":7651},"keywords":["Multidisciplinary Topics and Applications: MTA: Security and privacy","AI Ethics, Trust, Fairness: ETF: Trustworthy AI","AI Ethics, Trust, Fairness: ETF: Safety and robustness"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}