# Satellite Based Computing Networks with Federated Learning

arXiv: https://arxiv.org/abs/2111.10586 · HTML: https://mlsanity.com/paper/2111.10586/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 20 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Vision, Requirements, and Technology Trend of 6G: How to Tackle the Challenges of System Coverage, Capacity,User Data-Rate and Movement Speed](https://mlsanity.com/paper/2002.04929/) (2020)

## Built on by (most cited first, 25 of 25)

- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [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)
- [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)
- [Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation](https://mlsanity.com/paper/2309.01828/) (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)
- [From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2411.01312/) (2024)
- [Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression](https://mlsanity.com/paper/2211.14292/) (2022)
- [Energy-Aware Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2409.14832/) (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)
- [Heterogeneity: An Open Challenge for Federated On-board Machine Learning](https://mlsanity.com/paper/2408.06903/) (2024)
- [Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction](https://mlsanity.com/paper/2503.00154/) (2025)
- [Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks](https://mlsanity.com/paper/2504.10403/) (2025)
- [Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems](https://mlsanity.com/paper/2509.12222/) (2025)
- [orb-QFL: Orbital Quantum Federated Learning](https://mlsanity.com/paper/2509.16505/) (2025)
- [Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings](https://mlsanity.com/paper/2603.02233/) (2026)
- [SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning](https://mlsanity.com/paper/2608.24516/) (2026)

## Abstract (arXiv)

Driven by the ever-increasing penetration and proliferation of data-driven applications, a new generation of wireless communication, the sixth-generation (6G) mobile system enhanced by artificial intelligence (AI), has attracted substantial research interests. Among various candidate technologies of 6G, low earth orbit (LEO) satellites have appealing characteristics of ubiquitous wireless access. However, the costs of satellite communication (SatCom) are still high, relative to counterparts of ground mobile networks. To support massively interconnected devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we propose federated learning (FL) in LEO-based satellite communication networks. We first review the state-of-the-art LEO-based SatCom and related machine learning (ML) techniques, and then analyze four possible ways of combining ML with satellite networks. The learning performance of the proposed strategies is evaluated by simulation and results reveal that FL-based computing networks improve the performance of communication overheads and latency. Finally, we discuss future research topics along this research direction.