Spectral Gradient Orthogonalization Improves Differentially Private Training at Scale
Differentially private training adds isotropic Gaussian noise to clipped gradients, corrupting every singular direction equally. In vision models, where spatial correlation concentrates gradient energy into a low-rank subspace, most of this noise falls in directions that carry little signal. Spectral gradient orthogonalization via polar decomposition is introduced as a post-processing step that recovers directional signal from the noisy gradient's low-rank structure at zero additional privacy cost. A phase transition governs the utility of this approach: orthogonalization improves accuracy only when the per-direction spectral signal-to-noise ratio (SNR) suffices for singular vector recove...
| Authors | Sabari Shanmugam, Nick Barnes, Kerry Taylor |
|---|---|
| Primary category | cs.CV |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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