Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 31 Oct 2024.
Builds on
11 of 28 references.
References · 11
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
- 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
- 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
- 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
- 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
- 2021Satellite Based Computing Networks with Federated Learningcited by 25
- 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
- 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
- 2022Federated Learning in Satellite Constellationscited by 21
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Citing papers per year: 2025: 1.
1 paper.