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…