FedDOSE用联邦学习处理多中心脑影像数据，在ABIDE和ADHD-200上自闭症、多动症识别都超过现有方法，做医疗AI的可以看看。
FedDOSE是一个面向多站点fMRI数据的联邦学习框架，显式分解站点差异，用于动态功能连接（dFC）分析。它引入模块引导的Tucker分解块来编码高维dFC张量，提取模块级时空模式，并用最优传输重心与Procrustes分析对齐全局类别原型。在ABIDE-I、ABIDE-II和ADHD-200三个数据集上，FedDOSE在自闭症谱系障碍（ASD）和注意缺陷多动障碍（ADHD）诊断中的表现均优于现有方法。
FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity
Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well. While Federated Learning (FL) offers a privacy-preserving paradigm for collaborative training, standard approaches continue to struggle with statistical heterogeneity. In particular, site differences pose a key challenge in multi-site data settings. Additionally, existing FL approaches for fMRI rely on static Functional Connectivity ( FC), omitting dynamic information in brain networks. To address this, we propose FedDOSE, a novel framework that explicitly decomposes site differences for analysis of dynamic FC (dFC). FedDOSE introduces a Modularity-Guided Tucker Decomposition block to encode high-dimensional dFC tensors and capture modular-level spatio-temporal patterns efficiently. Class-specific prototypes are generated across all sites and subsequently aligned at the global level by using a combination of Optimal Transport (OT) barycenter formulation and Procrustes analysis. Extensive experiments for diagnosing Autism Spectrum Disorder (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) on three multi-site resting-state fMRI datasets: ABIDE-I, ABIDE-II, and ADHD-200, demonstrate that FedDOSE outperforms state-of-the-art methods in ASD and ADHD detection. Our results highlight its effectiveness in learning robust representations from multi-site datasets for reliable analysis.