Clinical diagnosis and subtype differentiation of neurodevelopmental and neuropsychiatric disorders, such as Attention-Deficit/Hyperactivity Disorder (ADHD), are still difficult to diagnose because behavioural rating scales can be subjective and the underlying neurobiology is highly variable. The Resting-state fMRI(rs-fMRI) can provide objective measures, but building reliable deep learning models is quite challenging because these models require large amounts of data from multiple hospitals, and they may vary in quality and characteristics. However, centralising sensitive brain images across hospitals is restricted by strict data privacy regulations (e.g., HIPAA, GDPR). Furthermore, multi-center datasets suffer from technical batch effects (scanner-induced noise),and biological subtype confounding (opposing neural signatures between Combined and Inattentive subtypes). This paper presents a comprehensive comparative taxonomy evaluating five primary learning paradigms: Isolated Single-Site Training, Centralised Pooled Training, Standard Horizontal Federated Learning (FedAvg/FedProx), Domain-Disentangled Federated Learning (DAFed), and Personalised/Subtype-Aware Federated Learning (FedNeuro/FedDOSE/TVFed-P). Each paradigm is systematically evaluated across privacy guarantees, resistance to scanner noise, subtype disambiguation, cross-site generalizability, communication overhead, and viability for hospital deployment. This taxonomy forms the essential architectural needs for deploying a privacy-preserving AI model for subtype identification in clinical hospital environments.
 

Federated Learning, Neuropsychiatric Phenotyping, ADHD Subtyping, Functional MRI, Scanner Batch Effects, Data Privacy, Comparative Taxonomy