mlsanityData to 7 Oct 2026

Federated Learning in Satellite Constellations

arXiv 2206.00307 ↗First posted 1 Jun 2022cs.IT cs.LG cs.NIcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

12 of 24 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2021A Field Guide to Federated Optimizationcited by 312
  4. 2019Asynchronous Federated Optimizationcited by 264
  5. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
  6. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  7. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  8. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  9. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  10. 2019LEO Small-Satellite Constellations for 5G and Beyond-5G Communicationscited by 10
  11. 2022NGSO Constellation Design for Global Connectivitycited by 5
  12. 2021Inter-Plane Inter-Satellite Connectivity in LEO Constellations: Beam Switching vs. Beam Steeringcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 10; 2025: 3; 2026: 5.

21 papers.

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  1. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  2. 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routingcited by 6
  3. 2022Selected Trends in Artificial Intelligence for Space Applicationscited by 5
  4. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  5. 2023Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbitcited by 4
  6. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approachcited by 4
  7. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  8. 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
  9. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  10. 2024Adversarial Machine Learning Threats to Spacecraftcited by 1
  11. 2024Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationscited by 1
  12. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
9 more
  1. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 1
  2. 2025Bringing Federated Learning to Spacecited by 1
  3. 2026Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communicationcited by 1
  4. 2024Sparse Incremental Aggregation in Multi-Hop Federated Learningcited by 0
  5. 2024Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellitescited by 0
  6. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  7. 2026Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?cited by 0
  8. 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0
  9. 2026Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellationscited by 0