Satellite Federated Edge Learning: Architecture Design and Convergence Analysis

arXiv:2404.01875v1 Announce Type: cross
Abstract: The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and …

abstract analysis architecture arxiv bandwidth concerns convergence cs.dc cs.it cs.lg data design distributed earth edge eess.sp leads low machine machine learning math.it networks privacy processing satellite sensing server type vast