# Ground-Assisted Federated Learning in LEO Satellite Constellations

arXiv: https://arxiv.org/abs/2109.01348 · HTML: https://mlsanity.com/paper/2109.01348/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [LEO Small-Satellite Constellations for 5G and Beyond-5G Communications](https://mlsanity.com/paper/1912.08110/) (2019)

## Built on by (most cited first, 30 of 31)

- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022)
- [AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms](https://mlsanity.com/paper/2212.11522/) (2022)
- [Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling](https://mlsanity.com/paper/2302.13447/) (2023)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes](https://mlsanity.com/paper/2305.12316/) (2023)
- [Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation](https://mlsanity.com/paper/2303.16071/) (2023)
- [Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM](https://mlsanity.com/paper/2401.00685/) (2024)
- [Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading](https://mlsanity.com/paper/2312.15361/) (2023)
- [Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning](https://mlsanity.com/paper/2401.15541/) (2024)
- [Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach](https://mlsanity.com/paper/2410.13602/) (2024)
- [Energy-Aware Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2409.14832/) (2024)
- [Scheduling for On-Board Federated Learning with Satellite Clusters](https://mlsanity.com/paper/2402.09105/) (2024)
- [FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks](https://mlsanity.com/paper/2412.17231/) (2024)
- [SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework](https://mlsanity.com/paper/2504.13479/) (2025)
- [Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networks](https://mlsanity.com/paper/2303.14351/) (2023)
- [Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2411.00263/) (2024)
- [Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks](https://mlsanity.com/paper/2501.15995/) (2025)
- [Bringing Federated Learning to Space](https://mlsanity.com/paper/2511.14889/) (2025)
- [Sparse Incremental Aggregation in Multi-Hop Federated Learning](https://mlsanity.com/paper/2407.18200/) (2024)
- [Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks](https://mlsanity.com/paper/2504.10403/) (2025)
- [Intelligent Spectrum Management in Satellite Communications](https://mlsanity.com/paper/2509.00286/) (2025)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)
- [Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloads](https://mlsanity.com/paper/2605.04052/) (2026)
- [FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations](https://mlsanity.com/paper/2608.03436/) (2026)
- [FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations](https://mlsanity.com/paper/2608.09687/) (2026)
- [ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal](https://mlsanity.com/paper/2608.24073/) (2026)

## Abstract (arXiv)

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.