arXiv AI
Sep 1

On the Plasticity Collapse in Continual Machine Unlearning

The paper investigates continual machine unlearning, where models must forget data over time. It identifies a fundamental issue called plasticity collapse, where successive unlearning requests cause geometric constraints that saturate parameter space, leading to two failure modes: forward failure (reduced forgetting quality) and backward failure (re‑memorization). Experiments across architectures and datasets confirm that plasticity collapse is a pervasive problem in continual unlearning.

By Yingdan Shi, Xiang Xu, Kaize Ding, Alfred O. Hero, Ren Wang