FI-LDP-HGAT applies feature-importance-aware anisotropic local differential privacy to a hierarchical graph attention network, recovering 81.5% utility at epsilon=4 and 0.762 defect recall at epsilon=2 on a DED porosity dataset while outperforming standard LDP and DP-SGD baselines.
Spatially-informed online prediction of milling surface deformation using multiphysics- infused graph neural network for digital twinning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
FI-LDP-HGAT applies feature-importance-aware anisotropic local differential privacy to a hierarchical graph attention network, recovering 81.5% utility at epsilon=4 and 0.762 defect recall at epsilon=2 on a DED porosity dataset while outperforming standard LDP and DP-SGD baselines.