Proceedings of the Annual Meeting of the Cognitive Science Society

Neuroimaging, especially through Functional Magnetic Resonance Imaging (fMRI), plays a pivotal role in understanding brain activity by leveraging blood-oxygen level dependent (BOLD) signals to estimate neural activities across the brain. The interpretation of these signals through functional connectivity (FC) matrices facilitates the application of Graph Neural Networks (GNN) for analyzing brain network structures, offering insights into both normal and abnormal brain functions. Despite the potential of centralized learning methods in this domain, challenges related to data privacy and the feasibility of sharing sensitive medical datasets across institutions limit their application. This study introduces the Federated Graph Learning Framework for Brain Connectome (FGLBC), addressing these concerns. This novel approach enables the collaborative training of GNN models across multiple entities, such as hospitals, without compromising data privacy. The FGLBC framework implements a privacy-preserving local GNN training (PPGT) algorithm that incorporates Differential Privacy (DP) to safeguard sensitive information during model training. Furthermore, we introduce a unique similarity-weighted aggregation (SWA) algorithm that enhances the aggregation process, thereby boosting the global model's utility and performance. Our comprehensive evaluation across benchmark datasets demonstrates that the FGLBC not only preserves user privacy but also achieves or surpasses the performance of existing methods.