High-altitude platform stations (HAPS) enable large-scale federated learning (FL) in non-terrestrial networks (NTN) by providing wide-area coverage and predominantly line-of-sight (LoS) connectivity to many ground users. However, practical deployments face heterogeneous and non-independently and identically distributed (non-IID) client data, which degrades accuracy and slows convergence. We propose a weighted attribute-based client selection strategy that leverages server-side indicators: historical traffic behavior, instantaneous channel quality, computational capability, and prior-round learning contribution. At each round, the HAPS computes a composite score and selects the top clients, while adapting attribute weights online based on their correlation with validation-loss improvement. We further provide theoretical justification that traffic-derived uniformity can serve as a proxy for latent data heterogeneity, enabling selection of client subsets with reduced expected non-IIDness. Simulations demonstrate improved test accuracy, faster convergence, and lower training loss compared with random, resource-only, and single-attribute baselines.

翻译：高空平台站（HAPS）通过提供广域覆盖和与众多地面用户之间以视距（LoS）为主的连接，支持非地面网络（NTN）中的大规模联邦学习（FL）。然而实际部署中面临客户端数据异构且非独立同分布（non-IID）的问题，这会降低模型精度并减慢收敛速度。本文提出一种基于加权属性的客户端选择策略，利用服务器端指标：历史流量行为、瞬时信道质量、计算能力及先前轮次的学习贡献。在每一轮中，HAPS计算综合得分并选择排名靠前的客户端，同时根据各属性与验证损失改善的相关性在线调整属性权重。我们进一步从理论上论证，基于流量导出的均匀性可代理潜在的数据异构性，从而选择预期非独立同分布程度降低的客户端子集。仿真结果表明，与随机、仅考虑资源和单一属性基准相比，本方法实现了更高的测试精度、更快的收敛速度和更低的训练损失。