Orthogonal Soft Pruning for Efficient Class Unlearning (2506.19891v1)

Abstract: Machine unlearning aims to selectively remove class-specific knowledge from pretrained neural networks to satisfy privacy regulations such as the GDPR. Existing methods typically face a trade-off between unlearning speed and preservation of predictive accuracy, often incurring either high computational overhead or significant performance degradation on retained classes. In this paper, we propose a novel class-aware soft pruning framework leveraging orthogonal convolutional kernel regularization to achieve rapid and precise forgetting with millisecond-level response times. By enforcing orthogonality constraints during training, our method decorrelates convolutional filters and disentangles feature representations, while efficiently identifying class-specific channels through activation difference analysis. Extensive evaluations across multiple architectures and datasets demonstrate stable pruning with near-instant execution, complete forgetting of targeted classes, and minimal accuracy loss on retained data. Experiments on CIFAR-10, CIFAR-100, and TinyImageNet confirm that our approach substantially reduces membership inference attack risks and accelerates unlearning by orders of magnitude compared to state-of-the-art baselines. This framework provides an efficient, practical solution for real-time machine unlearning in Machine Learning as a Service (MLaaS) scenarios.