Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity

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.