Dynamic Personalized Federated Learning Framework for Diverse LEO Satellite Networks

Résumé fourni par la source

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.

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