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papersSEP 11 04:00 UTC

DP-Muon: Differentially Private Optimization with Matrix-Orthogonalized Momentum

A new arXiv paper introduces DP-Muon, an optimizer that combines matrix-orthogonalized momentum with differential privacy guarantees. The method relies on standard per-example gradient clipping and releases one Gaussian-noised gradient per step, treating the matrix and auxiliary updates as post-processing. The authors present a convergence analysis for this approach.

arXiv DP-Muon Differential privacy Gaussian noise matrix-orthogonalized-momentum per-example-gradient-clipping

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arXiv cs.LGDP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum ↗SEP 11 04:00 UTC