Capturing Spectral and Spatial Patterns for Federated Remote Sensing Segmentation

Federated remote sensing (RS) semantic segmentation en-ables privacy-preserving collaborative training of dense prediction mod-els. However, its performance is severely hindered by the structured het-erogeneity inherent in RS image: sensor diversity causes distinct spec-tral shifts, while geographic variability induces spatial shifts. Existingmethods treat client heterogeneity as an unstructured distributional mis-match, failing to accommodate these coupled, RS-specific variations.To address this, we propose FedDap, a federated prototype adapta-tion framework that disentangles domain-conditioned variations from in-variant class semantics via Domain-Adaptive Prototypes (DAPs). DAPstransform globally shared semantic anchors through two specialized mod-ulation branches: a spectral branch for radiometric alignment and a spa-tial branch for multi-scale structural adaptation. A shift-aware gatingmechanism dynamically fuses these branches, producing class-conditionalprototypes tailored to local sensing and regional contexts. By employ-ing a pixel-to-prototype contrastive objective, we align local representa-tions with both the shared semantic references and the DAPs, effectivelymitigating cross-client mismatch. Extensive experiments across diversefederated RS benchmarks demonstrate that our method consistently out-performs state-of-the-art baselines.