Adaptive Differential Privacy for Satellite Image Recognition with Convergence-Guaranteed Optimization

Differential privacy (DP) has become a cornerstone for privacy-preserving machine learning, yet its application to high-resolution satellite imagery remains underexplored. Existing DP algorithms, such as DP-SGD, often rely on static noise levels and global clipping thresholds, which lead to slow convergence and poor utility in deep neural networks. In this paper, we propose ADP-SIR, an Adaptive Differential Privacy framework for Satellite Image Recognition with provable convergence guarantees. ADP-SIR introduces two novel components: Convergence-Guided Noise Scaling (CGNS), which dynamically adjusts the noise multiplier based on training stability, and Layerwise Sensitivity Profiling (LSP), which enables fine-grained clipping at the layer level. We provide theoretical analysis showing that ADP-SIR achieves good convergence in non-convex settings under Rényi differential privacy. Empirically, we evaluate ADP-SIR on EuroSAT and RESISC45, demonstrating significant improvements over DP-SGD and AdaClip-DP in terms of accuracy, convergence speed, and per-class fairness. Our framework bridges the gap between practical performance and rigorous privacy for remote sensing applications.