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 heterogeneity issue and class imbalance remain a significant barrier to rapid model convergence. In this paper, we explore the influence of heterogeneity on class imbalance, which diminishes performance in Aerial and Space Networks (ASNs)-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 ASNs-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 ASNs-based federated learning and that prevailing algorithms often fail to effectively address high levels of heterogeneity.