FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driven by orbital geometry. Global aggregation performs poorly when orbit-level class distributions are disjoint, while strong personalisation can be excessive when these distributions overlap. We present FedOrbit, which combines continuous orbit-level training over inter-satellite links, class-aware hierarchical aggregation, quality-weighted feature aggregation with return-rate dampening, and adaptive feature decomposition based on inter-orbit class similarity. Across three remote-sensing benchmarks and two non-IID partitions, FedOrbit achieves the highest accuracy in five of six settings and is within 0.9 percentage points of the best result in the sixth. The gains over the strongest baseline reach 16.1 percentage points under Dirichlet partitioning and 8.6 under pathological partitioning, with the smallest per-orbit accuracy spread in five of six settings.