A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks

• URL: http://arxiv.org/abs/2411.06137v1
• Date: Sat, 09 Nov 2024 10:22:52 GMT
• Title: A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks
• Authors: Wenbo Wu, Cheng Tan, Kangcheng Yang, Zhishu Shen, Qiushi Zheng, Jiong Jin,
• Abstract summary: Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. As commercial use expands, LEO satellite networks face heightened cyberattack risks. We propose a sharded blockchain-based federated learning framework for LEO networks, called SBFL-LEO.
• Score: 4.034610694515541
• License:
• Abstract: Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. However, as commercial use expands, LEO satellite networks face heightened cyberattack risks, especially through satellite-to-satellite communication links, which are more vulnerable than ground-based connections. As the number of operational satellites continues to grow, addressing these security challenges becomes increasingly critical. Traditional approaches, which focus on sending models to ground stations for validation, often overlook the limited communication windows available to LEO satellites, leaving critical security risks unaddressed. To tackle these challenges, we propose a sharded blockchain-based federated learning framework for LEO networks, called SBFL-LEO. This framework improves the reliability of inter-satellite communications using blockchain technology and assigns specific roles to each satellite. Miner satellites leverage cosine similarity (CS) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify malicious models and monitor each other to detect inaccurate aggregated models. Security analysis and experimental results demonstrate that our approach outperforms baseline methods in both model accuracy and energy efficiency, significantly enhancing system robustness against attacks.

Related papers

• SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework [19.59862482196897]
  We propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework. SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth. Experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2024-09-20T13:44:00Z)
• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Satellite Federated Edge Learning: Architecture Design and Convergence Analysis [47.057886812985984]
  This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation.
  arXiv  Detail & Related papers  (2024-04-02T11:59:58Z)
• FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks [18.213174641216884]
  A large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. We propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
  arXiv  Detail & Related papers  (2023-11-02T14:47:06Z)
• Updated Standard for Secure Satellite Communications: Analysis of Satellites, Attack Vectors, Existing Standards, and Enterprise and Security Architectures [0.0]
  There is a considerable gap in the industry regarding a generic security standard framework for satellite communication and space data systems. This project report will focus on identifying, categorizing, comparing, and assessing elements, threat landscape, enterprise security architectures, and available public standards of satellite communication and space data systems.
  arXiv  Detail & Related papers  (2023-10-29T18:39:23Z)
• Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation [1.4952056744888915]
  This paper proposes FedSecure, a secure FL approach designed for LEO constellations. FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites. It also reduces convergence delay drastically from days to only a few hours, yet achieving high accuracy of up to 85.35%.
  arXiv  Detail & Related papers  (2023-09-04T21:36:46Z)
• Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
  We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH) eRACH is a model-free approach that emerges through interaction with the non-stationary network environment. Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
  arXiv  Detail & Related papers  (2021-12-03T07:44:45Z)
• Deep Learning Aided Routing for Space-Air-Ground Integrated Networks Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
  Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
  arXiv  Detail & Related papers  (2021-10-28T14:12:10Z)
• Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy [1.4620086904601468]
  A security deficiency in the physical layer is addressed with a conceptual framework, considering the cyber-physical nature of the satellite systems. A learning-driven detection scheme is proposed, and the lightweight convolutional neural network (CNN) is designed. The results show that deficiency attacks against the satellite systems can be detected by employing the proposed scheme.
  arXiv  Detail & Related papers  (2021-07-10T17:04:22Z)
• Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
  This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
  arXiv  Detail & Related papers  (2021-01-25T13:01:16Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.