The deployment of federated learning (FL) within vertical heterogeneous networks, such as those enabled by high-altitude platform station (HAPS), offers the opportunity to engage a wide array of clients, each endowed with distinct communication and computational capabilities. This diversity not only enhances the training accuracy of FL models but also hastens their convergence. Yet, applying FL in these expansive networks presents notable challenges, particularly the significant non-IIDness in client data distributions. Such data heterogeneity often results in slower convergence rates and reduced effectiveness in model training performance. Our study introduces a client selection strategy tailored to address this issue, leveraging user network traffic behaviour. This strategy involves the prediction and classification of clients based on their network usage patterns while prioritizing user privacy. By strategically selecting clients whose data exhibit similar patterns for participation in FL training, our approach fosters a more uniform and representative data distribution across the network. Our simulations demonstrate that this targeted client selection methodology significantly reduces the training loss of FL models in HAPS networks, thereby effectively tackling a crucial challenge in implementing large-scale FL systems.

翻译：在垂直异构网络（例如由高空平台站（HAPS）支持的异构网络）中部署联邦学习（FL），为接入具有不同通信和计算能力的广泛客户端提供了契机。这种多样性不仅提升了FL模型的训练精度，还加速了其收敛速度。然而，在这些大规模网络中应用FL面临显著挑战，尤其是客户端数据分布中严重的非独立同分布（Non-IID）问题。这种数据异质性通常导致收敛速度减慢及模型训练性能下降。本研究提出了一种针对该问题的客户端选择策略，该策略利用用户网络流量行为，在优先保护用户隐私的前提下，基于客户端网络使用模式进行预测与分类。通过选择性吸纳数据呈现相似模式的客户端参与FL训练，本方法促进了网络中更均匀且更具代表性的数据分布。仿真结果表明，该目标导向的客户端选择方法显著降低了HAPS网络中FL模型的训练损失，从而有效解决了大规模FL系统实施中的关键难题。