Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
Letting hospitals train AI together without exposing patient brain data
Researchers created a system that lets multiple hospitals train a shared machine-learning model on EEG brain scans without sending anyone's actual patient data to a central server. The method uses mathematical masking and secret-sharing to hide individual hospital updates even from the aggregation server, and works whether you trust that server or assume it might cheat.
Hospitals can now collaborate on better AI models for brain disorders while keeping sensitive neurological data private and on-site. The tradeoff is real—adding strong privacy protections slows things down and requires more computation—but the semi-honest version adds only modest overhead, making it practical for real healthcare networks.