As low Earth orbit (LEO) satellite constellations expand, the volume of on-orbit data processing increases significantly. However, these systems face challenges in data processing due to heterogeneous data distribution and limited onboard resources. This paper presents an innovative framework for personalized federated learning (PFL) tailored to heterogeneous LEO satellite networks, which mitigates data processing challenges and optimizes distributed computational performance across the satellite constellation. Our approach introduces personalized models built on individual satellite datasets, coupled with dynamic model aggregation and pruning techniques for efficient training. By overcoming data heterogeneity and localizing global models, our method significantly improves performance over traditional approaches. Extensive simulation validates the effectiveness of our PFL framework, which demonstrates its superiority in integrating FL with model pruning in satellite networks. Simulation results show that our proposed PFL method outperforms four comparative algorithms, which highlights its effectiveness in addressing the unique challenges of LEO satellite networks.

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,ANovel DecentraLized PersonAlized Federated Learning for HeterogeNeous LEO SatellIte CoNstEllation (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.