Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. However, the ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore, federated learning (FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, to enable FL on LEO satellites, we still face three critical challenges that are i) heterogeneous computing and memory capabilities, ii) limited uplink rate, and iii) model staleness. To this end, we propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. To further demonstrate the effectiveness of the FedSN, we evaluate it using space modulation recognition and remote sensing image classification tasks by leveraging the data from real-world satellite networks. Extensive experimental results demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks and the effectiveness of each components in FedSN.

翻译：近期，SpaceX等商业公司在太空中成功发射并部署了大量低地球轨道（LEO）卫星。由于LEO卫星搭载了多模态传感器，它们不仅用于通信，还服务于各类机器学习应用，如空间调制识别、遥感图像分类等。然而，地面站（GS）与LEO卫星的接触时间有限（例如5分钟），可能无法下载如此大量的原始传感数据进行集中式模型训练。因此，联邦学习（FL）作为一种通过设备端训练解决该问题的方案应运而生。但遗憾的是，要在LEO卫星上实施FL，我们仍面临三大关键挑战：i）异构计算与内存能力、ii）有限的上行传输速率、iii）模型过时问题。为此，我们提出FedSN这一通用FL框架以应对上述挑战，并充分挖掘LEO卫星上的数据多样性。具体而言，我们首先提出一种新颖的子结构方案，考虑LEO卫星上不同的计算、内存和通信约束，实现异构本地模型训练。此外，我们提出一种伪同步模型聚合策略，动态调度模型聚合以补偿模型过时。为进一步验证FedSN的有效性，我们利用真实卫星网络数据，基于空间调制识别和遥感图像分类任务对其进行评估。大量实验结果表明，FedSN框架相比当前最先进的基准方法取得了更高的准确率、更低的计算和通信开销，同时验证了FedSN中各组件的有效性。