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

TL;DR

This paper introduces SBFL-LEO, a sharded blockchain-based federated learning framework designed to improve security, reliability, and efficiency in LEO satellite networks against cyberattacks.

Contribution

It presents a novel blockchain-enabled federated learning framework tailored for LEO satellites, incorporating role assignment and malicious model detection techniques.

Findings

Outperforms baseline methods in model accuracy

Enhances energy efficiency of satellite communications

Improves robustness against cyberattacks

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.