# Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling

## TL;DR

This paper introduces FedLEO, a federated learning framework for LEO satellite constellations that enhances model convergence and accuracy by intra-plane communication and optimized sink satellite scheduling.

## Contribution

FedLEO innovatively combines intra-plane model propagation with sink satellite scheduling to improve federated learning efficiency in satellite networks.

## Key findings

- FedLEO significantly speeds up FL convergence.
- FedLEO increases model accuracy compared to existing solutions.
- FedLEO outperforms state-of-the-art benchmarks.

## Abstract

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising concept. This paper proposes FedLEO, a novel federated learning (FL) framework that realizes the concept of SEC and overcomes the limitation (slow convergence) of existing FL-based solutions. FedLEO (1) augments the conventional FL's star topology with ``horizontal'' intra-plane communication pathways in which model propagation among satellites takes place; (2) optimally schedules communication between ``sink'' satellites and the GS by exploiting the predictability of satellite orbiting patterns. We evaluate FedLEO extensively and benchmark it with the state of the art. Our results show that FedLEO drastically expedites FL convergence, without sacrificing -- in fact it considerably increases -- the model accuracy.