Low Earth Orbit (LEO) satellite constellations have seen significant growth and functional enhancement in recent years, which integrates various capabilities like communication, navigation, and remote sensing. However, the heterogeneity of data collected by different satellites and the problems of efficient inter-satellite collaborative computation pose significant obstacles to realizing the potential of these constellations. Existing approaches struggle with data heterogeneity, varing image resolutions, and the need for efficient on-orbit model training. To address these challenges, we propose a novel decentralized PFL framework, namely, A Novel Decentra L ized Person A lized Federated Learning for Heteroge N eous LEO Satell I te Co N st E llation (ALANINE). ALANINE incorporates decentralized FL (DFL) for satellite image Super Resolution (SR), which enhances input data quality. Then it utilizes PFL to implement a personalized approach that accounts for unique characteristics of satellite data. In addition, the framework employs advanced model pruning to optimize model complexity and transmission efficiency. The framework enables efficient data acquisition and processing while improving the accuracy of PFL image processing models. Simulation results demonstrate that ALANINE exhibits superior performance in on-orbit training of SR and PFL image processing models compared to traditional centralized approaches. This novel method shows significant improvements in data acquisition efficiency, process accuracy, and model adaptability to local satellite conditions.

翻译：近年来，低地球轨道（LEO）卫星星座在规模与功能上均取得显著增长与增强，集通信、导航、遥感等多种能力于一体。然而，不同卫星采集数据的异构性以及星间高效协同计算问题，严重制约了此类星座潜力的充分发挥。现有方法在应对数据异构性、图像分辨率差异以及高效在轨模型训练需求方面面临诸多困难。为应对这些挑战，本文提出一种新型去中心化个性化联邦学习框架，即面向异构低地球轨道卫星星座的新型去中心化个性化联邦学习框架（ALANINE）。ALANINE融合去中心化联邦学习（DFL）技术以提升卫星图像超分辨率（SR）质量，从而改善输入数据品质；继而采用个性化联邦学习（PFL）方法，以兼顾卫星数据的独有特征。此外，该框架通过先进模型剪枝技术优化模型复杂度与传输效率。该框架在实现高效数据采集与处理的同时，提升了PFL图像处理模型的精度。仿真实验表明，相较于传统集中式方法，ALANINE在超分辨率与PFL图像处理模型的在轨训练中展现出更优性能。这一新方法在数据采集效率、处理精度及模型对本地卫星环境的适应性方面均取得显著提升。