mlsanityData to 7 Oct 2026

Scheduling for On-Board Federated Learning with Satellite Clusters

arXiv 2402.09105 ↗First posted 14 Feb 2024cs.DC cs.LGcited by 2

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Abstract (arXiv)

Mega-constellations of small satellites have evolved into a source of massive amount of valuable data. To manage this data efficiently, on-board federated learning (FL) enables satellites to train a machine learning (ML) model collaboratively without having to share the raw data. This paper introduces a scheme for scheduling on-board FL for constellations connected with intra-orbit inter-satellite links. The proposed scheme utilizes the predictable visibility pattern between satellites and ground station (GS), both at the individual satellite level and cumulatively within the entire orbit, to mitigate intermittent connectivity and best use of available time. To this end, two distinct schedulers are employed: one for coordinating the FL procedures among orbits, and the other for controlling those within each orbit. These two schedulers cooperatively determine the appropriate time to perform global updates in GS and then allocate suitable duration to satellites within each orbit for local training, proportional to usable time until next global update. This scheme leads to improved test accuracy within a shorter time.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 20 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

18 of 20 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2020Federated Learning with Matched Averagingcited by 564
  4. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  5. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  6. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  7. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  8. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  9. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  10. 2022Federated Learning in Satellite Constellationscited by 21
  11. 2022A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updatescited by 17
  12. 2019LEO Small-Satellite Constellations for 5G and Beyond-5G Communicationscited by 10
6 more
  1. 2022Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 9
  2. 2023Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellationcited by 6
  3. 2022NGSO Constellation Design for Global Connectivitycited by 5
  4. 2021LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?cited by 4
  5. 2023Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbitcited by 4
  6. 2019Inter-plane satellite matching in dense LEO constellationscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routingcited by 6
  2. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3