HYPERBOLIC MODEL AGGREGATION FOR FEDERATED LEARNING IN FMRI

The privacy-sensitive nature of clinical data often limits the use of machine learning in medical imaging applications, particularly for modalities with high acquisition costs such as functional MRI (fMRI). Federated learning mitigates data-sharing barriers by training site-specific models locally and aggregating weights centrally into a server model. However, small and heterogeneous per-site samples in medical imaging heighten the need for robust model-aggregation strategies. In this work, we introduce a federated aggregation scheme based on hyperbolic geometry to provide a robust and flexible approach to federated model weight integration. The proposed scheme is plug-and-play for standard federated learning loops. Empirically, our method improves stability and accuracy across multi-site fMRI data from ABIDE I, yielding more consistent convergence versus methods based on Euclidean mean and median. Codes are publicly available at https://github.com/Jiyao96/FedHAvg.