Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity 基于自模式连通性的流形表示机器遗忘近似实现

Background

Machine unlearning aims to remove the influence of specific data points from trained models while preserving overall learning performance. Existing methods like label manipulation and task-gradient reversal are limited in their effectiveness and can undermine the original learning objectives, often failing to guarantee equivalent performance to retraining.

Key Points

ManiF-SMC (Manifold Forgetting with Self Mode Connectivity) addresses these limitations by reformulating unlearning as pushing erased samples away from their learned manifold representation centroid towards their nearest semantic neighbors. This approach is implemented through a margin-based triplet loss that incorporates a self-mode-connectivity module to adaptively generate margins for each case.

ManiF-SMC Reformulation

ManiF-SMC recasts the approximate unlearning problem by pushing erased samples away from their original learned manifold representation centroid and towards their nearest semantic neighbors in the retained data. This aligns unlearning with retraining behavior, operating purely within the model's representation space to minimize reliance on labels and task-specific gradients.

Self-Mode-Connectivity Module

A key component of ManiF-SMC is the self-mode-connectivity module, which rapidly reconstructs local manifolds to guide adaptive margin generation. Finding a suitable margin for unlearning is challenging, so this module ensures effective and efficient unlearning by adapting margins based on the local manifold structure.

Significance

ManiF-SMC demonstrates comparable unlearning effectiveness to state-of-the-art approximate methods while operating solely within the model's representation space. This approach provides a more robust and effective mechanism for machine unlearning without compromising the original learning objectives, making it a significant advancement in privacy-preserving machine learning techniques.

背景与问题

机器卸载机制是保障“被遗忘权”的基础。目前大多数研究依赖于标签操纵或任务反向梯度来实现卸载，但这些方法往往效果有限，并且可能损害原始学习目标。此外，它们通常无法确保与重新训练标准卸载的一致性。

核心内容

ManiF-SMC 提出了一种新的卸载策略 ManiF，该策略通过将被删除样本从其原始语义中心移向保留数据的最近邻居来工作。这种重构使得卸载过程与重新训练的行为一致，并且完全在特征空间中进行操作，从而减少对标签和特定任务梯度的需求。

为了应对基于流形表示的卸载问题，ManiF-SMC 将卸载目标和特征保持目标整合到一个基于边距的三重损失中。由于找到合适的边距对于卸载来说具有挑战性，研究者提出了自模式连接性模块来快速重建局部流形，并指导每个卸载案例的适应性边距生成。

意义与影响

ManiF-SMC 的方法能够在不依赖标签和特定任务梯度的情况下实现高效的机器卸载。实验结果表明，它在四个代表性数据集上的表现与最先进的近似卸载方法相当，且仅在模型特征空间中操作。这不仅提高了卸载的有效性，还减少了对原始学习目标的影响，并提供了更通用的解决方案来处理卸载问题。