Satellite Federated Edge Learning: Architecture Design and Convergence
  Analysis
Yuanming Shi Li Zeng Jingyang Zhu Yong Zhou Chunxiao Jiang Khaled B. Letaief
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
short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL.
Notably, frequent model transmission between the satellites and ground incurs
prolonged waiting time and large transmission latency. This paper introduces a
novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation
networks. By integrating inter-satellite links (ISL) for intra-orbit model
aggregation, the proposed algorithm significantly reduces the usage of low data
rate and intermittent GSL. Our proposed method includes a ring all-reduce based
intra-orbit aggregation mechanism, coupled with a network flow-based
transmission scheme for global model aggregation, which enhances transmission
efficiency. Theoretical convergence analysis is provided to characterize the
algorithm performance. Extensive simulations show that our FEDMEGA algorithm
outperforms existing satellite FEEL algorithms, exhibiting an approximate 30%
improvement in convergence rate.