FedOrbit：面向非独立同分布（Non-IID）低轨（LEO）卫星星座的自适应个性化联邦学习
FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations
AI总结：
FedOrbit针对LEO卫星星座的Non-IID数据问题，结合多类技术提升联邦学习性能，在遥感基准上较基线有显著准确率提升且误差小。
AI中文摘要：
低轨（LEO）卫星星座中的联邦学习（FL）受轨道几何驱动的非独立同分布（Non-IID）数据和不规则地面站可见性影响。当轨道级类别分布不相交时，全局聚合表现较差；而当这些分布重叠时，过度个性化又可能冗余。我们提出FedOrbit，它结合了基于星间链路的连续轨道级训练、类别感知分层聚合、带返回率阻尼的质量加权特征聚合，以及基于轨道间类别相似性的自适应特征分解。在三个遥感基准和两个Non-IID划分下，FedOrbit在六个设置中的五个达到最高准确率，第六个设置与最佳结果的差距在0.9个百分点以内；在Dirichlet划分下，其较最强基准的提升达16.1个百分点，在病态划分下达8.6个百分点，且在六个设置中的五个实现最小的轨道级准确率差值。
英文摘要：
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.
