Federated learning offers a compelling solution to the challenges of networking and data privacy within aerial and space networks by utilizing vast private edge data and computing capabilities accessible through drones, balloons, and satellites. While current research has focused on optimizing the learning process, computing efficiency, and minimizing communication overhead, the issue of heterogeneity and class imbalance remains a significant barrier to rapid model convergence. In our study, we explore the influence of heterogeneity on class imbalance, which diminishes performance in ASN-based federated learning. We illustrate the correlation between heterogeneity and class imbalance within grouped data and show how constraints such as battery life exacerbate the class imbalance challenge. Our findings indicate that ASN-based FL faces heightened class imbalance issues even with similar levels of heterogeneity compared to other scenarios. Finally, we analyze the impact of varying degrees of heterogeneity on FL training and evaluate the efficacy of current state-of-the-art algorithms under these conditions. Our results reveal that the heterogeneity challenge is more pronounced in ASN-based federated learning and that prevailing algorithms often fail to effectively address high levels of heterogeneity.

翻译：联邦学习通过利用无人机、气球和卫星可访问的海量私有边缘数据与计算能力，为空天网络中的组网与数据隐私挑战提供了极具前景的解决方案。尽管当前研究主要聚焦于优化学习过程、提升计算效率并降低通信开销，但数据异构性与类别不平衡问题仍是阻碍模型快速收敛的重要障碍。本研究深入探究了异构性对类别不平衡的影响机制，该影响会显著降低基于空天网络的联邦学习性能。我们阐明了分组数据中异构性与类别不平衡之间的相关性，并揭示了电池续航等约束条件如何加剧类别不平衡的挑战。研究结果表明，与其他场景相比，即使面临相似程度的异构性，基于空天网络的联邦学习仍会遭遇更严重的类别不平衡问题。最后，我们系统分析了不同程度异构性对联邦学习训练的影响，并评估了当前先进算法在此类条件下的有效性。实验结果表明：异构性挑战在基于空天网络的联邦学习中更为突出，且现有算法往往难以有效应对高度异构的场景。