On the Plasticity Collapse in Continual Machine Unlearning

Machine unlearning enables deep neural networks to selec-tively remove the influence of specific data in response to privacy andregulatory requirements. While prior work largely studies single-shot un-learning, real-world systems must accommodate continual unlearning,where multiple unlearning requests occur sequentially over time. In thiswork, we identify a fundamental limitation of this setting: plasticity col-lapse, a progressive breakdown in a model’s ability to effectively forget.Through theoretical analysis of continual unlearning dynamics, we showthat continual unlearning operations accumulate geometric constraintsin parameter space, leading to saturated subspaces that restrict futureupdates. This structural effect induces two distinct failure modes: (1)Forward failure – diminishing forgetting quality for subsequent tasks,and (2) Backward failure – spontaneous re-memorization of previouslyforgotten information. Extensive experiments across multiple architec-tures, datasets, and methods in image classification confirm that plastic-ity collapse is not an artifact of specific implementations, but a pervasivephenomenon inherent to continual unlearning. Our findings reveal a criti-cal barrier to the long-term reliability of machine unlearning systems andmotivate the development of plasticity-preserving unlearning algorithms.Our code is available at https://github.com/TIML-Group/Continual-Machine-Unlearning-Plasticity-Collapse.