Decoupling Direction and Magnitude in Federated Remote Sensing Object Detection

Federated learning provides a practical paradigm for collaborative remote sensing object detection without centralizing raw data, yet it is severely hindered by client heterogeneity. This heterogeneity manifests as a dual bottleneck: local detectors struggle to isolate discriminative object cues from complex aerial backgrounds, and conflicting classifier updates across clients cause destructive vector cancellation during aggregation, which shrinks classifier norms and severely attenuates prediction confidence. To address this, we propose CoRD-Fed, a dual-level federated framework that synergistically resolves both issues. On the client side, an object-centric region distillation module restricts knowledge transfer to informative target regions via ground-truth-constrained matching and CAM-guided spatial–channel alignment, effectively suppressing background interference. On the server side, a True Energy-Restored Consensus Aggregation (TERCA) strategy explicitly decouples update direction from magnitude. It estimates category-wise directional consensus to suppress conflicting gradients, then restores the aggregated classifier norm using the uncompromised sum of individual update energies, thereby preventing confidence collapse. Extensive experiments on DIOR and DOTA under severe non-IID partitioning demonstrate that CoRD-Fed consistently outperforms state-of-the-art baselines, yielding notable improvements in small-object detection and average recall.