SPACECHAIN: A Blockchain-Integrated Federated Learning Framework for Low-Earth Orbit Satellite Networks

Driven by the rapid development of low-Earth orbit (LEO) satellite networks, machine learning (ML) model training and data processing face significant challenges owing to constrained communication bandwidth (BW) and privacy concerns. Federated learning (FL) has emerged as a promising solution for in situ data processing; however, centralized FL techniques are vulnerable to single-point failures and malicious attacks. To address these issues, we propose SpaceChain, a blockchain-enhanced FL framework tailored to LEO satellite networks. The SpaceChain framework includes a decentralized validation system for assessing local model updates. The system implements a proof-of-stake (PoS)-inspired consortium consensus mechanism in a permissioned blockchain system, thereby giving precedence to satellites based on cumulative learning contributions for validation, leading to a reduction of the energy expenditure when compared with a proof-of-work (PoW) protocol. FL maintains the privacy of data, with raw data stored on satellites, and the blockchain maintains tamper resistance in coordinating model updates. Satellites are assigned specific roles during each communication round, including ML nodes for local updates, consensus verifiers for validation, and blockchain stake validators for the final model integration. This role-based architecture ensures dynamic node participation and enhances the reliability and efficiency of the system. The simulation results demonstrate that the SpaceChain framework enhances the security, reliability, and performance of ML training in LEO networks. This article outlines the principles, architecture, and potential applications of SpaceChain, providing a foundation for secure and efficient ML training in next-generation satellite communication systems.