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，商业公司（如SpaceX）已在太空成功发射并部署了大量低轨卫星。由于低轨卫星搭载多模态传感器，其不仅服务于通信，还支持多种机器学习应用，如空间调制识别、遥感图像分类等。然而，地面站与低轨卫星的接触时间有限（例如5分钟），导致其无法下载如此海量的原始传感数据用于集中式模型训练。因此，联邦学习作为一种通过设备端训练解决该问题的有前景方案应运而生。然而，在低轨卫星上部署联邦学习仍面临三个关键挑战：i）异构计算与存储能力、ii)有限的上行链路速率、iii）模型陈旧性。为此，我们提出FedSN这一通用联邦学习框架，旨在应对上述挑战并充分挖掘低轨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，能够根据低轨卫星上不同的计算、存储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，通过动态调度模型聚合来补偿模型陈旧性。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，与现有最优基准方法相比，FedSN框架在实现更高精度的同时，显著降低了计算与通信开销，并验证了其各组件的有效性。