  The primary goal of this project is to develop privacy-preserving machine
learning model training techniques for fNIRS data. This project will build a
local model in a centralized setting with both differential privacy (DP) and
certified robustness. It will also explore collaborative federated learning to
train a shared model between multiple clients without sharing local fNIRS
datasets. To prevent unintentional private information leakage of such clients'
private datasets, we will also implement DP in the federated learning setting.
