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.

翻译：本项目的主要目标是开发针对fNIRS数据的隐私保护机器学习模型训练技术。该项目将在集中式环境下构建具有差分隐私（DP）和认证鲁棒性的本地模型。同时，项目将探索协作式联邦学习，使多个客户端在不共享本地fNIRS数据集的情况下训练共享模型。为防止此类客户端私有数据集的无意隐私信息泄露，我们还将联邦学习环境中实施差分隐私（DP）。