mlsanityData to 7 Oct 2026

Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks

arXiv 2512.08882 ↗First posted 9 Dec 2025cs.CR cs.LGcited by 1

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Abstract (arXiv)

The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered by massive data from commercial and governmental low Earth orbit (LEO) satellites. Federated satellite learning (FSL) enables joint model training without sharing raw data, but suffers from slow convergence due to intermittent connectivity and introduces critical trust challenges--where biased or falsified updates can arise across satellite constellations, including those injected through cyberattacks on inter-satellite or satellite-ground communication links. We propose OrbitChain, a blockchain-backed framework that empowers trustworthy multi-vendor collaboration in LEO networks. OrbitChain (i) offloads consensus to high-altitude platforms (HAPs) with greater computational capacity, (ii) ensures transparent, auditable provenance of model updates from different orbits owned by different vendors, and (iii) prevents manipulated or incomplete contributions from affecting global FSL model aggregation. Extensive simulations show that OrbitChain reduces computational and communication overhead while improving privacy, security, and global model accuracy. Its permissioned proof-of-authority ledger finalizes over 1000 blocks with sub-second latency (0.16,s, 0.26,s, 0.35,s for 1-of-5, 3-of-5, and 5-of-5 quorums). Moreover, OrbitChain reduces convergence time by up to 30 hours on real satellite datasets compared to single-vendor, demonstrating its effectiveness for real-time, multi-vendor learning. Our code is available at https://github.com/wsu-cyber-security-lab-ai/OrbitChain.git

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 46 references that are in the map. First posted on arXiv 9 Dec 2025.

Builds on

27 of 46 references.

References · 27

  1. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 138
  5. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 46
  6. 2022The Fundamental Price of Secure Aggregation in Differentially Private Federated Learningcited by 39
  7. 2019BAFFLE : Blockchain Based Aggregator Free Federated Learningcited by 38
  8. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  9. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  10. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  11. 2022Satellite edge computing for real-time and very-high resolution Earth observationcited by 12
  12. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
15 more
  1. 2023FedGSM: Efficient Federated Learning for LEO Constellations with Gradient Staleness Mitigationcited by 8
  2. 2020Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Frameworkcited by 7
  3. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
  4. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  5. 2022Future Space Networks: Toward the Next Giant Leap for Humankindcited by 6
  6. 2023Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellationcited by 6
  7. 2023Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregationcited by 6
  8. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  9. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  10. 2022Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Aheadcited by 4
  11. 2024SOK: Blockchain for Provenancecited by 2
  12. 2023ESAFL: Efficient Secure Additively Homomorphic Encryption for Cross-Silo Federated Learningcited by 1
  13. 2024A Novel Non-Terrestrial Networks Architecture: All Optical LEO Constellations with High-Altitude Ground Stationscited by 1
  14. 2024A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networkscited by 1
  15. 2025When Secure Aggregation Falls Short: Achieving Long-Term Privacy in Asynchronous Federated Learning for LEO Satellite Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellationscited by 0