2024/09/23 by Nasrin Razmi, Razmi, Nasrin, Bho Matthiesen +5
Computer Science · Engineering · #Distributed #Distributed systems and fault tolerance #FOS: Computer and information sciences #FOS: Electrical engineering #IoT Networks and Protocols #Machine Learning (cs.LG) #Parallel #Satellite Communication Systems #Signal Processing (eess.SP) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.14832
openalex publication_date 2024/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.