mlsanityData to 7 Oct 2026

Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation

arXiv 2309.01828 ↗First posted 4 Sep 2023cs.CR cs.LGcited by 6

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Abstract (arXiv)

Satellite technologies have advanced drastically in recent years, leading to a heated interest in launching small satellites into low Earth orbit (LEOs) to collect massive data such as satellite imagery. Downloading these data to a ground station (GS) to perform centralized learning to build an AI model is not practical due to the limited and expensive bandwidth. Federated learning (FL) offers a potential solution but will incur a very large convergence delay due to the highly sporadic and irregular connectivity between LEO satellites and GS. In addition, there are significant security and privacy risks where eavesdroppers or curious servers/satellites may infer raw data from satellites' model parameters transmitted over insecure communication channels. To address these issues, this paper proposes FedSecure, a secure FL approach designed for LEO constellations, which consists of two novel components: (1) decentralized key generation that protects satellite data privacy using a functional encryption scheme, and (2) on-orbit model forwarding and aggregation that generates a partial global model per orbit to minimize the idle waiting time for invisible satellites to enter the visible zone of the GS. Our analysis and results show that FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites. It is lightweight with significantly lower communication and computation overheads than other privacy-preserving FL aggregation approaches. It also reduces convergence delay drastically from days to only a few hours, yet achieving high accuracy of up to 85.35% using realistic satellite images.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 4 Sep 2023.

Builds on

10 of 20 references.

References · 10

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
  3. 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 46
  4. 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
  5. 2021Satellite Based Computing Networks with Federated Learningcited by 25
  6. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  7. 2022FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPscited by 21
  8. 2022AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platformscited by 18
  9. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  10. 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 2.

6 papers.

  1. 2024Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDMcited by 6
  2. 2024Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learningcited by 5
  3. 2024CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networkscited by 1
  4. 2025Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networkscited by 1
  5. 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
  6. 2026Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellationscited by 0