Article,
Ground-Assisted Federated Learning in LEO Satellite Constellations
Affiliations
- [1] Univ Bremen, Dept Commun Engn, D-28359 Bremen, Germany [NORA names: Germany; Europe, EU; OECD];
- [2] Aalborg Univ, Dept Elect Syst, DK-9100 Aalborg, Denmark [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]
Abstract
In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets. To address this problem, we propose a new set of algorithms based on Federated learning (FL), including a novel asynchronous FL procedure based on FedAvg that exhibits better robustness against heterogeneous scenarios than the state-of-the-art. Extensive numerical evaluations based on MNIST and CIFAR-10 datasets highlight the fast convergence speed and excellent asymptotic test accuracy of the proposed method.
Keywords
Collaborative work,
Computational modeling,
Delays,
Low earth orbit satellites,
Satellite communication,
Satellites,
Task analysis,
Training,
federated optimization,
low earth orbit (LEO)