Energy-Aware Federated Learning in Satellite Constellations
This addresses energy management for reliable satellite operation and battery longevity in space networks, representing an incremental improvement in domain-specific optimization.
The paper tackled the problem of energy consumption in federated learning for satellite constellations by proposing an energy-aware computation time scheduler, achieving a more than 3x increase in battery lifetime compared to energy-agnostic scheduling.
Federated learning in satellite constellations, where the satellites collaboratively train a machine learning model, is a promising technology towards enabling globally connected intelligence and the integration of space networks into terrestrial mobile networks. The energy required for this computationally intensive task is provided either by solar panels or by an internal battery if the satellite is in Earth's shadow. Careful management of this battery and system's available energy resources is not only necessary for reliable satellite operation, but also to avoid premature battery aging. We propose a novel energy-aware computation time scheduler for satellite FL, which aims to minimize battery usage without any impact on the convergence speed. Numerical results indicate an increase of more than 3x in battery lifetime can be achieved over energy-agnostic task scheduling.