A Review of Distributed Large Models in Space-Air-Ground Integrated Networks

Abstract

The space-air-ground integrated network offers a forward-looking direction for alleviating ground computing power bottlenecks. Efficiently deploying large, distributed models, a key technology for 6G ubiquitous intelligence, in highly dynamic, resource-heterogeneous environments holds significant strategic and application value. This paper systematically reviews the current state of research, core challenges, and key technologies for constructing distributed large-scale models in space-air-ground integrated networks. It analyzes critical issues,, including network heterogeneity, on-board resource constraints, and the security-efficiency trade-off, and evaluates the strengths, limitations, and applicability of existing solutions. The study reveals that adaptive model partitioning, lightweight security protocols, and standardized frameworks remain major shortcomings. Future research should focus on lightweight model design and architecture standardization, deep integration of privacy computing with communication, and joint scheduling of communication, sensing, and computing. This paper clarifies current research limitations and future directions, offering references for the large-scale application of space-air-ground integrated computing power networks.