Efficient Federated Edge Learning for Satellite Mega-Constellations: Architecture Design and Convergence Analysis

The core message of this paper is to propose a novel federated edge learning (FEEL) algorithm, named FEDMEGA, tailored for low-earth-orbit (LEO) satellite mega-constellation networks. The proposed FEDMEGA algorithm leverages the superior characteristics of inter-satellite links (ISLs) to significantly reduce the usage of low-data-rate and intermittent ground-to-satellite links (GSLs), thereby enhancing the transmission efficiency and convergence rate of the FEEL training process.