Cocoon: A System Architecture for Differentially Private Training with Correlated Noises
Cocoon is a hardware-software framework designed to speed up differentially private training of machine learning models using correlated noises instead of standard methods. It tackles high overheads by pre-computing and storing noise efficiently (Cocoon-Emb) and uses custom near-memory processing hardware (CocoonNMP) to handle large models, resulting in significant training time reductions.