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：近年来，SpaceX等商业公司在太空成功发射并部署了大量低地球轨道（LEO）卫星。由于LEO卫星配备多模态传感器，它们不仅服务于通信，还用于多种机器学习应用，例如空间调制识别、遥感图像分类等。然而，地面站（GS）因与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用于集中式模型训练。因此，联邦学习（FL）作为一种有前景的解决方案，通过设备端训练来解决这一问题。不幸的是，要在LEO卫星上实现FL，仍然面临三大关键挑战：i)异构的计算和存储能力，ii)有限的上行链路速率，以及iii)模型陈旧性问题。为此，本文提出FedSN作为通用FL框架来应对上述挑战，并充分探索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、存储和通信约束，实现异构的本地模型训练。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陈旧性。为进一步展示FedSN的有效性，我们利用来自真实卫星网络的数据，通过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，与最先进的基准方法相比，FedSN框架在准确率上更高，计算和通信开销更低，且验证了FedSN中各组成部分的有效性。