FedMental: Topology-Aware Federated Prototype Learning for Polymorphic Multimodal Psychiatry

Psychiatric diagnosis is moving from subjective clinical ob-servation toward objective multimodal assessment using audio, visual,and textual signals. Federated learning offers a natural framework formulti-center collaboration without centralizing sensitive patient data,but psychiatric data introduce a form of non-IID heterogeneity thatis not captured by standard label or domain skew: patients with thesame diagnosis can exhibit highly polymorphic manifestations, form-ing multi-peak within-class distributions. Existing prototype-based FLmethods usually aggregate client or class features into coarse centroids,which can collapse distinct symptom modes into semantically ambigu-ous prototypes. We propose FedMental, a topology-aware federatedprototype framework for multi-center multimodal psychiatry. FedMen-tal first preserves subsampled sample-level embeddings before prema-ture client-mean aggregation, then applies Topology-Aware Proto-type Refinement (TAPR) on the server to discover class-view la-tent modes through parameter-free clustering. On the client side, Intra-View Mode Alignment (IVMA) softly anchors local representationsto matched mode prototypes, while Cross-View Semantic Contrast(CVSC) promotes view-invariant semantics across modalities and cen-ters. Experiments on the real-world Psycare psychiatric benchmark andtwo standard heterogeneous FL benchmarks show that FedMental consis-tently improves over strong FL baselines, achieving +8.83% over FedAvgon Psycare, +11.42% on Office-Caltech, and +4.41% on Digits.