Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has extensively studied the optimization of the learning process, computing efficiency, and communication overhead. An important yet often overlooked aspect is that participants contribute predictive knowledge with varying diversity of knowledge, affecting the quality of the learned federated models. In this paper, we propose a novel approach to address this issue by introducing a Weighted Averaging and Client Selection (WeiAvgCS) framework that emphasizes updates from high-diversity clients and diminishes the influence of those from low-diversity clients. Direct sharing of the data distribution may be prohibitive due to the additional private information that is sent from the clients. As such, we introduce an estimation for the diversity using a projection-based method. Extensive experiments have been performed to show WeiAvgCS's effectiveness. WeiAvgCS could converge 46% faster on FashionMNIST and 38% faster on CIFAR10 than its benchmarks on average in our experiments.

翻译：联邦学习在空天网络（ASNs）的数据传输与隐私约束下，提供了一种利用无人机、气球和卫星中大规模私有边缘数据的可行方法。现有研究已广泛探索了学习过程优化、计算效率及通信开销等课题。一个被忽视却至关重要的问题是：参与者贡献的预测知识具有不同的多样性程度，这将影响联邦学习模型的质量。本文提出了一种加权平均与客户端选择（WeiAvgCS）框架，通过强调高多样性客户端的更新并降低低多样性客户端的影响来解决该问题。由于直接共享数据分布可能泄露客户端的额外隐私信息，我们引入了一种基于投影的多样性估计方法。大量实验表明，WeiAvgCS在FashionMNIST数据集上的收敛速度平均比基准方法快46%，在CIFAR10数据集上快38%。