无访问权限的合成遗忘：一种面向机器遗忘的少样本零样本框架
Synthetic Forgetting without Access: A Few-shot Zero-glance Framework for Machine Unlearning
AI总结：
针对机器遗忘需访问原始训练数据的局限，提出少样本零样本设定下的GFOES框架，通过生成最优擦除样本和两阶段微调，仅用5%原始数据即可实现有效遗忘并保留模型性能。
摘要：
机器遗忘旨在从已训练模型中消除特定数据的影响，以确保隐私合规。然而，现有大多数方法都假设可以完全访问原始训练数据集，这在实际场景中往往难以实现。本文研究了一个更具现实意义但也更具挑战性的设定：少样本零样本设定，即仅能获取留存数据的一小部分，且遗忘集完全不可访问。本文提出了GFOES这一新型框架，包含生成反馈网络（GFN）和两阶段微调流程。GFN可合成最优擦除样本（OES），这类样本会在目标类别上诱发高损失，使模型无需访问原始遗忘数据即可遗忘特定类别的知识，同时保留在留存类别上的性能。两阶段微调流程在第一阶段实现激进遗忘，第二阶段进行效用恢复。在三个图像分类数据集上的实验表明，GFOES在仅使用5%原始数据的情况下，在logit层和表征层均实现了有效遗忘，同时保持了优异的性能。该框架为数据受限条件下的隐私保护机器学习提供了实用且可扩展的解决方案。
英文摘要：
Machine unlearning aims to eliminate the influence of specific data from trained models to ensure privacy compliance. However, most existing methods assume full access to the original training dataset, which is often impractical. We address a more realistic yet challenging setting: few-shot zero-glance, where only a small subset of the retained data is available and the forget set is entirely inaccessible. We introduce GFOES, a novel framework comprising a Generative Feedback Network (GFN) and a two-phase fine-tuning procedure. GFN synthesises Optimal Erasure Samples (OES), which induce high loss on target classes, enabling the model to forget class-specific knowledge without access to the original forget data, while preserving performance on retained classes. The two-phase fine-tuning procedure enables aggressive forgetting in the first phase, followed by utility restoration in the second. Experiments on three image classification datasets demonstrate that GFOES achieves effective forgetting at both logit and representation levels, while maintaining strong performance using only 5% of the original data. Our framework offers a practical and scalable solution for privacy-preserving machine learning under data-constrained conditions.
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。
