Enhancing Privacy Protection via Verifiable Chained Federated Learning for Space-Air-Ground Integrated Vehicle Network
Man Zhou, Lansheng Han, Xin Che · IEEE Transactions on Consumer Electronics · 2025
With the rapid advancement of 6G networks and artificial intelligence technologies, Space-Air-Ground Integrated Networks (SAGIN) are revolutionizing communication services. SAGIN seamlessly integrates satellite, Unmanned Aerial Vehicle (UAV), and terrestrial infrastructures to offer expansive coverage, ultra-low latency, and highly reliable connectivity. This paper introduces SACVTH, a novel chain-verifiable and trustworthy heterogeneous federated learning framework aimed at tackling critical challenges within the SAGIN context, including resource optimization, privacy protection, and security verification. In particular, we initially devise a sophisticated community partitioning mechanism tailored to task requirements and device capabilities to optimize resource utilization and ensure timely task execution. Subsequently, we propose an innovative lightweight verifiable secure aggregation protocol, that leverages sparse matrices and pseudorandom generators to compress verification data. Additionally, we implement a chain-based group broadcast mechanism that significantly reduces the frequency of direct communications and the volume of data transmissions between trainers and servers by facilitating intra-group model sharing and aggregation. This is achieved by enabling intra-group model sharing and aggregation, allowing trainers to communicate through intermediary relay nodes, thereby enhancing both communication efficiency and scalability within the federated learning framework. Rigorous security analysis demonstrates that SACVTH ensures the verifiability of aggregation results. Comparative evaluations with other state-of-the-art methods reveal that SACVTH protocol significantly outperforms others in resource optimization and security verification, leading to substantial improvements in the efficiency, robustness, and reliability of federated learning frameworks.