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.
Dlam: Deep learning based real-time poros- ity prediction for additive manufacturing using ther- mal images of the melt pool,
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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.