发表机构
Florida International University（佛罗里达国际大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出物理引导的梯度上升机器遗忘方法，通过物理残差约束权重更新，在IEEE 34节点系统上验证可同时移除投毒并恢复物理合规性。
AI 中文摘要
本文提出了一种基于物理引导的梯度上升机器遗忘方法，该方法将遗忘信号与目标信息物理系统的物理残差耦合，确保遗忘过程中的权重更新被引导至权重空间中物理可行的区域。物理残差在梯度上升过程中充当安全围栏：模型被引导远离被投毒的行为盆地，同时朝向符合物理规律的领域，而非朝向可能仍违反领域约束的任意替代区域。我们将所提方法与四种基线方法进行了评估：朴素梯度上升、精确遗忘、SISA以及基于IEEE 34节点配电系统的完整重训练，该系统由两个基于物理信息神经网络的分布式能源资源控制器驱动，并通过高保真OpenDSS潮流协同仿真进行验证。评估结果表明，我们提出的物理引导模型能够同时移除投毒效应并恢复物理合规性，这对于安全关键型信息物理系统的安全部署至关重要。
英文摘要
This paper proposes a physics-guided gradient-ascent-based machine unlearning method that couples the forgetting signal with the physical residual of the target cyber-physical systems, ensuring that weight updates during unlearning are steered toward physically feasible regions of the weight space. The physics residual acts as a safety fence during gradient ascent: the model is steered away from the poisoned behavioral basin and simultaneously toward physics-compliant territory, rather than toward an arbitrary alternative that may still violate domain constraints. We evaluate the proposed method against four baselines: naive gradient ascent, exact unlearning, SISA, and full retraining on an IEEE 34-bus distribution system, driven by two physics-informed neural network-based distribution energy resource controllers and validated through high-fidelity OpenDSS power-flow co-simulation. From the evaluation, we found that our proposed physics-guided model simultaneously removes poison and restores physical compliance, which are essential for the safe deployment of safety-critical cyber-physical systems
