Federated Conformal Prediction for Reliable Brain Disorder Subtyping

Federated Learning (FL) enables multi-site Functional Magnetic Resonance Imaging (fMRI) analyses to jointly learn shared brain connectivity patterns associated with neurological disorders while adapting to site-specific phenotypic and scanner variations. However, these frameworks often overlook uncertainty quantification for individual model predictions. In neuroimaging, models must alert clinicians when diagnostic predictions are uncertain or have low confidence. This becomes especially important for rare brain disorder subtypes, where limited training data can lead to unreliable yet overconfident predictions masked by high overall model accuracy. To address these issues, we propose BrainFCP (Brain Federated Conformal Prediction), a novel framework that applies Conformal Prediction (CP) in a federated multi-site fMRI setting to provide site-wise and subtype-specific uncertainty estimates with theoretical guarantees. We extensively evaluated our framework on two neurodevelopmental disorders: Attention-Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), using the ADHD-200 and ABIDE-I resting-state fMRI datasets, respectively. Our results demonstrate BrainFCP’s ability to construct valid personalized subtype-level prediction sets for each site in a federated setting.