Major depressive disorder is projected to become the leading contributor to mental illness by 2030. While resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a non-invasive solution for depression detection, two significant challenges remain. First, due to medical data privacy regulations and the high costs associated with acquiring the necessary equipment, individual medical institutions struggle to obtain sufficient annotated data. Second, domain shifts, caused by discrepancies in scanner parameters and acquisition protocols across multi-center datasets, significantly hinder model generalization. To address these challenges, we propose a federated domain adaptation (FDA) method that integrates co-activation patterns and a multimodal Mamba network, termed FDA-CAPMA, for fMRI-based depression detection. Specifically, a federated learning architecture ensures both physical data isolation and patient privacy through parameter aggregation. A state-space model-based Mamba network captures cross-modal correlations between fMRI time-series features and non-imaging features. Additionally, a local maximum mean discrepancy (LMMD) module aligns source and target domain distributions in both feature and prediction spaces. Extensive experiments on the largest multi-center depression dataset (Rest-meta-MDD, 1813 participants) and ABIDE dataset, our method achieves an accuracy of 67.16%, and 65.72%, respectively. This work establishes a new paradigm for privacy-preserving depression recognition. Code will be available at: https://github.com/helang818/FDA-CAPMA/ © 2026 Elsevier B.V.