A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy-friendly. However, FL requires many communication rounds between clients (satellites) and the parameter server (PS), leading to substantial delays of up to several days in LEO constellations. In this paper, we propose a novel one-shot FL approach for LEO satellites, called LEOShot, that needs only a single communication round to complete the entire learning process. LEOShot comprises three processes: (i) synthetic data generation, (ii) knowledge distillation, and (iii) virtual model retraining. We evaluate and benchmark LEOShot against the state of the art and the results show that it drastically expedites FL convergence by more than an order of magnitude. Also surprisingly, despite the one-shot nature, its model accuracy is on par with or even outperforms regular iterative FL schemes by a large margin

翻译：低地球轨道（LEO）卫星星座由大量在太空中高速飞行的小型卫星组成，这些卫星收集海量移动性数据，例如用于天气预报的云层移动、跨区域迁徙的大规模动物种群、森林火灾蔓延以及飞行器追踪。机器学习可用于分析这些移动性数据以应对全球挑战，而联邦学习（FL）作为一种有前景的方法，因其无需传输原始数据从而兼具带宽高效与隐私保护特性。然而，FL要求在客户端（卫星）与参数服务器（PS）之间进行多轮通信，导致低轨星座中长达数天的显著延迟。本文提出一种名为LEOShot的新型低轨卫星一次性联邦学习方法，仅需单次通信即可完成整个学习过程。LEOShot包含三个流程：（i）合成数据生成，（ii）知识蒸馏，以及（iii）虚拟模型重训练。我们评估并对比了LEOShot与现有最优方法的性能，结果表明其将联邦学习的收敛速度提升超过一个数量级。令人惊讶的是，尽管采用一次性框架，其模型准确率仍能与常规迭代式联邦学习方案相当，甚至以显著优势超越后者。