ToPPFed: Topological Prototype-Enhanced Personalized Federated Learning for Neuropsychiatric Disorders Identification

Functional connectivity networks (FCNs) derived from functional magnetic resonance imaging (fMRI) have been widely used to characterize topological alterations of brain networks in neuropsychiatric disorders (NDs). Given the frequent restrictions on direct multi-site fMRI data sharing, federated learning (FL) offers a collaborative modeling paradigm without exchanging raw neuroimaging data. However, conventional parameter-averaging FL approaches struggle under cross-site non-IID distributions. Prototype-based FL provides a promising alternative, yet existing designs implicitly rely on spatially structured image data and fail to capture the topology-centric semantics of FCNs. To bridge this gap, we propose ToPPFed, a Topological Prototype-Enhanced Personalized Federated Learning framework for multi-site classification between subjects with each studied disorder and normal controls (NCs). ToPPFed introduces a Graph Topological Prototype Learning module to extract discriminative topology-aware prototypes from FCNs and a Contrastive Mask-Induced Residual Scaling mechanism to adaptively integrate group-level priors into individual representations. By exchanging topology prototypes instead of raw data or full model parameters, ToPPFed supports cross-site collaboration while reducing direct data exposure. Experiments on multi-site fMRI datasets of three representative NDs show that ToPPFed improves accuracy (ACC) by 1.7-7.9 percentage points over the best-performing federated baseline on each dataset. Interpretability analyses indicate that ToPPFed highlights model-derived discriminative brain regions and functional connections. The topology-aware exchange of node and edge prototypes offers an effective framework for collaborative FCN modeling across imaging sites without centralizing neuroimaging data.