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math.OC2025
On the Inherent Privacy of Zeroth Order Projected Gradient Descent
Devansh Gupta, Meisam Razaviyayn, Vatsal Sharan
Differentially private zeroth-order optimization methods have recently gained popularity in private fine tuning of machine learning models due to their reduced memory requirements.…
cs.LG2025
Memory-Efficient Differentially Private Training with Gradient Random Projection
Alex Mulrooney, Devansh Gupta, James Flemings +4
Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead due to per-sample gradient clip…