mlsanityData to 14 Oct 2026

Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading

arXiv 2312.15361 ↗First posted 23 Dec 2023cs.DC cs.AIcited by 5

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Abstract (arXiv)

While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services. In this paper, we propose a ground-to-satellite cooperative federated learning (FL) methodology to facilitate machine learning service management over remote regions. Our methodology orchestrates satellite constellations to provide the following key functions during FL: (i) processing data offloaded from ground devices, (ii) aggregating models within device clusters, and (iii) relaying models/data to other satellites via inter-satellite links (ISLs). Due to the limited coverage time of each satellite over a particular remote area, we facilitate satellite transmission of trained models and acquired data to neighboring satellites via ISL, so that the incoming satellite can continue conducting FL for the region. We theoretically analyze the convergence behavior of our algorithm, and develop a training latency minimizer which optimizes over satellite-specific network resources, including the amount of data to be offloaded from ground devices to satellites and satellites' computation speeds. Through experiments on three datasets, we show that our methodology can significantly speed up the convergence of FL compared with terrestrial-only and other satellite baseline approaches.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 23 Dec 2023.

Builds on

32 of 58 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 367
  8. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  9. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  10. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  11. 2019Federated Learning over Wireless Fading Channelscited by 183
  12. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
20 more
  1. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  2. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  3. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
  4. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  5. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
  6. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  7. 2019MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Samplingcited by 67
  8. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  9. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  10. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  11. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  12. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  13. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  14. 2022Federated Learning in Satellite Constellationscited by 21
  15. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  16. 2020Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellationscited by 12
  17. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  18. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  19. 2022Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 9
  20. 2022Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Networkcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  2. 2024FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networkscited by 2
  3. 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1
  4. 2025Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Datacited by 0
  5. 2025Communication-Efficient Learning for Satellite Constellationscited by 0