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Communication-Efficient Learning for Satellite Constellations

arXiv 2511.20220 ↗First posted 25 Nov 2025cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. We focus on designing a novel, communication-efficient algorithm that still yields accurate trained models. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 25 Nov 2025.

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15 of 24 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
  5. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  6. 2022Towards Efficient Communications in Federated Learning: A Contemporary Surveycited by 24
  7. 2023Local Exact-Diffusion for Decentralized Optimization and Learningcited by 24
  8. 2022Can 5th Generation Local Training Methods Support Client Sampling? Yes!cited by 22
  9. 2022Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 9
  10. 2022A Snapshot of the Frontiers of Client Selection in Federated Learningcited by 9
  11. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  12. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
3 more
  1. 2024Communication-Efficient Distributed Learning with Local Immediate Error Compensationcited by 2
  2. 2024Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationscited by 1
  3. 2025Bringing Federated Learning to Spacecited by 1

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