面向鲁棒机器遗忘的学习式遗忘方法研究
Learning to Unlearn for Robust Machine Unlearning
AI总结：
针对机器遗忘中擦除特定数据与维持模型性能难以平衡的问题，提出基于元学习的LTU框架与梯度协调策略，仅用少量剩余数据就能兼顾知识保留与精准遗忘，提升了机器遗忘的效率与效果。
摘要：
机器遗忘（MU）旨在无需完整重新训练即可从已训练模型中移除特定数据样本的相关知识，该任务的难点在于需同时实现有效数据擦除与维持模型整体性能的双重目标。尽管该领域近期已有进展，但在遗忘的双重目标间取得平衡仍具挑战。本文从泛化的全新视角出发，提出一种新型学习式遗忘（Learning-to-Unlearn, LTU）框架，该框架采用元学习方法优化遗忘过程，以统一方式提升遗忘效果与记忆保留能力。LTU包含一套元优化方案，可助力模型仅利用剩余集的小子集就能有效保留可泛化知识，同时彻底遗忘特定数据样本。本文还提出一种梯度协调策略，通过缓解梯度冲突来对齐记忆与遗忘的优化轨迹，从而确保模型更新高效且有效。本文方法在机器遗忘任务中展现出更优的效率与效果，为数据权利与模型可复用性相关挑战提供了有前景的解决方案。
英文摘要：
Machine unlearning (MU) seeks to remove knowledge of specific data samples from trained models without the necessity for complete retraining, a task made challenging by the dual objectives of effective erasure of data and maintaining the overall performance of the model. Despite recent advances in this field, balancing between the dual objectives of unlearning remains challenging. From a fresh perspective of generalization, we introduce a novel Learning-to-Unlearn (LTU) framework, which adopts a meta-learning approach to optimize the unlearning process to improve forgetting and remembering in a unified manner. LTU includes a meta-optimization scheme that facilitates models to effectively preserve generalizable knowledge with only a small subset of the remaining set, while thoroughly forgetting the specific data samples. We also introduce a Gradient Harmonization strategy to align the optimization trajectories for remembering and forgetting via mitigating gradient conflicts, thus ensuring efficient and effective model updates. Our approach demonstrates improved efficiency and efficacy for MU, offering a promising solution to the challenges of data rights and model reusability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
