## 3 Federated Learning for Space-Air-Ground Integrated Networks  
### Space-Air-Ground Integrated Network Architecture  
The Space-Air-Ground Integrated Network vertically integrates space networks (including geostationary orbit satellites and low-orbit satellite constellations), aerial networks (including unmanned aerial vehicles, airships, and other high-altitude platforms), and terrestrial networks (including cellular networks and the Internet of Things), forming a unified three-dimensional information network system. Its core objective is to achieve global coverage, flexible mobility, and on-demand communication capabilities to meet the diverse requirements of future intelligent societies for communication networks.  
The SAGIN architecture integrates three different network resources (aerial, terrestrial, and space-based), and its network architecture can be divided into the following layers:  
Space-Based Network: Includes Low Earth Orbit (LEO), Medium Earth Orbit (MEO), Geostationary Earth Orbit (GEO) satellites, and space stations. These are interconnected via satellite constellations, supporting data forwarding and connecting remote regions and other networks, providing wide-area coverage and global communication capabilities.  
Aerial Network: Includes Unmanned Aerial Vehicles (UAVs), High-Altitude Platform Stations (HAPS), aircraft, and other platforms that can provide flexible and dynamic network coverage for local areas during movement or environmental changes. This layer is particularly suitable for temporary communication needs or disaster emergency scenarios.  
Terrestrial Network: Includes ground base stations, fiber optic networks, data centers, and other infrastructure that provides high-bandwidth, low-latency local communication services. The terrestrial layer is the primary data flow exchange layer in SAGINs, responsible for the transmission and management of most data.  
A schematic diagram of the Space-Air-Ground Integrated Network architecture is shown in Figure 1.  
The Space-Air-Ground Integrated Network (SAGIN) provides wide coverage, flexibility, and high bandwidth by integrating multiple network layers. However, implementing privacy-preserving federated learning in SAGINs faces challenges due to heterogeneity across layers, high latency, limited bandwidth, dynamic topology, and privacy concerns.  
Data privacy remains vulnerable despite federated learning's inherent protections, as SAGINs' multi-layer architecture (terrestrial, aerial, space-based) introduces complex security risks during data transmission.  
SAGINs' space-based and aerial layers suffer from significant latency and bandwidth limitations, hindering the efficient exchange of model updates and global models during federated learning. This inefficiency slows training and impacts both model performance and convergence. Additionally, SAGINs' dynamic topology changes create challenges for communication and aggregation mechanisms, as device disconnections can cause information loss and model inconsistency.  
Device and data heterogeneity in SAGINs produce substantial variations in model updates, complicating effective information integration. Non-IID data distributions across devices further complicate aggregation and impact training effectiveness. Network constraints often prevent synchronous model updates, making it difficult to maintain model consistency while preserving privacy.