GENIU：面向不平衡数据的受限数据访问遗忘方法
GENIU: A Restricted Data Access Unlearning for Imbalanced Data
AI总结：
针对受限数据访问且原始数据不平衡时的类别遗忘难题，本文提出GENIU框架，利用VAE同步生成各类代理样本并结合批内调优，在无需原始训练数据的情况下有效遗忘多数类且保持模型性能。
摘要：
随着数据隐私日益受到重视，机器遗忘的重要性显著提升。类别遗忘是指使已训练模型忘记此前学习过的特定类别数据；由于分类任务占据当今机器学习即服务（MLaaS）的大多数，类别遗忘尤为重要。在原始数据上重新训练模型并排除待遗忘数据（也称遗忘数据），是类别遗忘的常用方法。然而，在遗忘阶段并不总能保证获得原始数据，这促使人们研究受限数据访问下的类别遗忘。当前具有受限数据访问的遗忘方法通常通过已训练的神经网络分类器生成代理样本，但它们一般聚焦于训练数据和遗忘数据均平衡的情形。然而，不平衡的原始数据会给这些代理样本和遗忘过程带来麻烦，尤其是在遗忘数据主要由多数类构成时。为解决该问题，我们提出了 GENerative Imbalanced Unlearning（GENIU）框架。GENIU 利用变分自编码器（VAE）在训练原始模型的同时训练一个代理生成器。这些生成的代理能够准确表示每个类别，并在遗忘阶段被使用，从而消除对原始训练数据的依赖。为进一步缓解因遗忘多数类而导致的性能下降，我们引入了一种与生成代理协同工作的批内调优策略。GENIU 是首个在不平衡数据设置和受限数据访问下用于类别遗忘的实用框架，可确保为未来遗忘保留必要信息。实验结果证实 GENIU 优于现有方法，确立了其在经验场景中的有效性。
英文摘要：
With the increasing emphasis on data privacy, the significance of machine unlearning has grown substantially. Class unlearning, which involves enabling a trained model to forget data belonging to a specific class learned before, is important as classification tasks account for the majority of today's machine learning as a service (MLaaS). Retraining the model on the original data, excluding the data to be forgotten (a.k.a forgetting data), is a common approach to class unlearning. However, the availability of original data during the unlearning phase is not always guaranteed, leading to the exploration of class unlearning with restricted data access. While current unlearning methods with restricted data access usually generate proxy sample via the trained neural network classifier, they typically focus on training and forgetting balanced data. However, the imbalanced original data can cause trouble for these proxies and unlearning, particularly when the forgetting data consists predominantly of the majority class. To address this issue, we propose the GENerative Imbalanced Unlearning (GENIU) framework. GENIU utilizes a Variational Autoencoder (VAE) to concurrently train a proxy generator alongside the original model. These generated proxies accurately represent each class and are leveraged in the unlearning phase, eliminating the reliance on the original training data. To further mitigate the performance degradation resulting from forgetting the majority class, we introduce an in-batch tuning strategy that works with the generated proxies. GENIU is the first practical framework for class unlearning in imbalanced data settings and restricted data access, ensuring the preservation of essential information for future unlearning. Experimental results confirm the superiority of GENIU over existing methods, establishing its effectiveness in empirical scenarios.
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。
