mlsanityData to 14 Oct 2026

FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations

arXiv 2202.01267 ↗First posted 2 Feb 2022cs.LG cs.DC stat.MLcited by 28

Not on the map. Why?

Abstract (arXiv)

Large-scale deployments of low Earth orbit (LEO) satellites collect massive amount of Earth imageries and sensor data, which can empower machine learning (ML) to address global challenges such as real-time disaster navigation and mitigation. However, it is often infeasible to download all the high-resolution images and train these ML models on the ground because of limited downlink bandwidth, sparse connectivity, and regularization constraints on the imagery resolution. To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites. We show fundamental challenges in applying existing FL algorithms among satellites and ground stations, and we formulate an optimization problem which captures a unique trade-off between staleness and idleness. We propose a novel FL framework, named FedSpace, which dynamically schedules model aggregation based on the deterministic and time-varying connectivity according to satellite orbits. Extensive numerical evaluations based on real-world satellite images and satellite networks show that FedSpace reduces the training time by 1.7 days (38.6%) over the state-of-the-art FL algorithms.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 2 Feb 2022.

Builds on

16 of 36 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2018Group Normalizationcited by 1,524
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2019Towards Federated Learning at Scale: System Designcited by 832
  7. 2018A Survey on Deep Transfer Learningcited by 419
  8. 2017Functional Map of the Worldcited by 338
  9. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
  10. 2019Asynchronous Federated Optimizationcited by 267
  11. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  12. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
4 more
  1. 2016Distributed Mean Estimation with Limited Communicationcited by 136
  2. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  3. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  4. 2020Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noisecited by 21

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 7; 2025: 4; 2026: 4.

28 papers.

Sort:

By area:

  1. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  2. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  3. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  4. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  5. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  6. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  7. 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
  8. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  9. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  10. 2023FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusioncited by 8
  11. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  12. 2023Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellationcited by 6
16 more
  1. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  2. 2023CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centerscited by 6
  3. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  4. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  5. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  6. 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
  7. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  8. 2024Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationscited by 1
  9. 2025Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networkscited by 1
  10. 2025EarthSight: A Distributed Framework for Low-Latency Satellite Intelligencecited by 1
  11. 2022Detection and Prevention Against Poisoning Attacks in Federated Learningcited by 0
  12. 2025Communication-Efficient Learning for Satellite Constellationscited by 0
  13. 2026Constraint-Aware Execution Planning for Hybrid Space-Ground Compute Workloadscited by 0
  14. 2026FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellationscited by 0
  15. 2026FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellationscited by 0
  16. 2026When Is Availability-Aware Training Worth It? A Benchmark and Empirical Study of Interruption-Resilient Optimization Under Predictable Compute Schedulescited by 0