In the context of machine unlearning, the primary challenge lies in
effectively removing traces of private data from trained models while
maintaining model performance and security against privacy attacks like
membership inference attacks. Traditional gradient-based unlearning methods
often rely on extensive historical gradients, which becomes impractical with
high unlearning ratios and may reduce the effectiveness of unlearning.
Addressing these limitations, we introduce Mini-Unlearning, a novel approach
that capitalizes on a critical observation: unlearned parameters correlate with
retrained parameters through contraction mapping. Our method, Mini-Unlearning,
utilizes a minimal subset of historical gradients and leverages this
contraction mapping to facilitate scalable, efficient unlearning. This
lightweight, scalable method significantly enhances model accuracy and
strengthens resistance to membership inference attacks. Our experiments
demonstrate that Mini-Unlearning not only works under higher unlearning ratios
but also outperforms existing techniques in both accuracy and security,
offering a promising solution for applications requiring robust unlearning
capabilities.

Mini-Unlearning 是一种轻量级、可扩展的方法，通过最小的一部分历史梯度以及收缩映射来有效地实现高比例的 machine unlearning，提高模型准确性并增强对隐私攻击的抵抗力。