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 achiev...
| Authors | Satwat Bashir, Tasos Dagiuklas, Muddesar Iqbal |
|---|---|
| Primary category | cs.LG |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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