# Federated Learning in Satellite Constellations

arXiv: https://arxiv.org/abs/2206.00307 · HTML: https://mlsanity.com/paper/2206.00307/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [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)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2109.01348/) (2021)
- [Satellite Based Computing Networks with Federated Learning](https://mlsanity.com/paper/2111.10586/) (2021)
- [Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities](https://mlsanity.com/paper/2104.07557/) (2021)
- [On-Board Federated Learning for Dense LEO Constellations](https://mlsanity.com/paper/2111.12769/) (2021)
- [LEO Small-Satellite Constellations for 5G and Beyond-5G Communications](https://mlsanity.com/paper/1912.08110/) (2019)
- [NGSO Constellation Design for Global Connectivity](https://mlsanity.com/paper/2203.16597/) (2022)
- [Inter-Plane Inter-Satellite Connectivity in LEO Constellations: Beam Switching vs. Beam Steering](https://mlsanity.com/paper/2106.06203/) (2021)

## Built on by (most cited first, 21 of 21)

- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [Continual Deep Reinforcement Learning for Decentralized Satellite Routing](https://mlsanity.com/paper/2405.12308/) (2024)
- [Selected Trends in Artificial Intelligence for Space Applications](https://mlsanity.com/paper/2212.06662/) (2022)
- [Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading](https://mlsanity.com/paper/2312.15361/) (2023)
- [Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit](https://mlsanity.com/paper/2305.04059/) (2023)
- [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)
- [Adversarial Machine Learning Threats to Spacecraft](https://mlsanity.com/paper/2405.08834/) (2024)
- [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)
- [Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication](https://mlsanity.com/paper/2601.17419/) (2026)
- [Sparse Incremental Aggregation in Multi-Hop Federated Learning](https://mlsanity.com/paper/2407.18200/) (2024)
- [Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites](https://mlsanity.com/paper/2411.17831/) (2024)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)
- [Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?](https://mlsanity.com/paper/2604.19399/) (2026)
- [FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations](https://mlsanity.com/paper/2608.09687/) (2026)
- [Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2609.31932/) (2026)

## Abstract (arXiv)

Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity. This paper presents the new context brought to FL by satellite constellations, where the connectivity patterns are significantly different from the ones observed in conventional terrestrial FL. The focus is on large constellations in low Earth orbit (LEO), where each satellites participates in a data-driven FL task using a locally stored dataset. This scenario is motivated by the trend towards mega constellations of interconnected small satellites in LEO and the integration of artificial intelligence in satellites. We propose a classification of satellite FL based on the communication capabilities of the satellites, the constellation design, and the location of the parameter server. A comprehensive overview of the current state-of-the-art in this field is provided and the unique challenges and opportunities of satellite FL are discussed. Finally, we outline several open research directions for FL in satellite constellations and present some future perspectives on this topic.