DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum
Researchers have introduced DP-MacAdam, a new algorithm designed to enhance privacy in machine learning training. This method combines adaptive clipping and adaptive momentum techniques, using the same gradient variance estimates for both processes. The algorithm aims to improve model utility over existing methods like DP-SGD and DP-Adam without requiring manual tuning of the clipping threshold. AI
IMPACT Introduces a novel algorithm for more effective privacy-preserving machine learning training.