  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.
