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

本文提出了一种新颖的 LEO 卫星一次通信轮方式的 FL 方法 --LEOShot，通过三个步骤：合成数据生成，知识蒸馏，虚拟模型重训练，以及与现有方法相比的评估和基准测试，结果显示 LEOShot 大大加快了 FL 的收敛速度，并且在精度上与常规的迭代 FL 方案相当甚至有很大优势。