mlsanityData to 14 Oct 2026

FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

arXiv 2608.09687 ↗First posted 10 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driven by orbital geometry. Global aggregation performs poorly when orbit-level class distributions are disjoint, while strong personalisation can be excessive when these distributions overlap. We present FedOrbit, which combines continuous orbit-level training over inter-satellite links, class-aware hierarchical aggregation, quality-weighted feature aggregation with return-rate dampening, and adaptive feature decomposition based on inter-orbit class similarity. Across three remote-sensing benchmarks and two non-IID partitions, FedOrbit achieves the highest accuracy in five of six settings and is within 0.9 percentage points of the best result in the sixth. The gains over the strongest baseline reach 16.1 percentage points under Dirichlet partitioning and 8.6 under pathological partitioning, with the smallest per-orbit accuracy spread in five of six settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 27 references that are in the map. First posted on arXiv 10 Aug 2026.

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References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2017Remote Sensing Image Scene Classification: Benchmark and State of the Artcited by 797
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  9. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  10. 2020Adaptive Personalized Federated Learningcited by 358
  11. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  12. 2020Satellite Communications in the New Space Era: A Survey and Future Challengescited by 36
7 more
  1. 2021Ground-Assisted Federated Learning in LEO Satellite Constellationscited by 31
  2. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  3. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  4. 2022Federated Learning in Satellite Constellationscited by 21
  5. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  6. 2019LEO Small-Satellite Constellations for 5G and Beyond-5G Communicationscited by 10
  7. 2024ALANINE: A Novel Decentralized Personalized Federated Learning For Heterogeneous LEO Satellite Constellationcited by 4

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