Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
This work addresses the practical deployment of federated learning for satellite data processing, though it appears incremental as it builds on existing FL methods with domain-specific adaptations.
The paper tackles the challenge of deploying federated learning in satellite constellations to address the satellite downlink deficit, introducing AutoFLSat which reduces model training time by 12.5% to 37.5% compared to alternatives.
Space has emerged as an exciting new application area for machine learning, with several missions equipping deep learning capabilities on-board spacecraft. Pre-processing satellite data through on-board training is necessary to address the satellite downlink deficit, as not enough transmission opportunities are available to match the high rates of data generation. To scale this effort across entire constellations, collaborated training in orbit has been enabled through federated learning (FL). While current explorations of FL in this context have successfully adapted FL algorithms for scenario-specific constraints, these theoretical FL implementations face several limitations that prevent progress towards real-world deployment. To address this gap, we provide a holistic exploration of the FL in space domain on several fronts. 1) We develop a method for space-ification of existing FL algorithms, evaluated on 2) FLySTacK, our novel satellite constellation design and hardware aware testing platform where we perform rigorous algorithm evaluations. Finally we introduce 3) AutoFLSat, a generalized, hierarchical, autonomous FL algorithm for space that provides a 12.5% to 37.5% reduction in model training time than leading alternatives.