Air-Ground Model Collaboration for Low-Altitude Intelligent Networks with Heterogeneous Computational Resources

The Low-Altitude Intelligent Network (LAIN) serves as a critical enabler for low-altitude economic development, showing significant potential in areas such as environmental monitoring. These applications typically require extensive computational resources to drive large-scale models, posing challenges to resource-constrained aerial platforms like unmanned aerial vehicles (UAVs). Although air-ground model collaboration has been studied by some early studies, most approaches assume homogeneous computational resources, overlooking the heterogeneity among UAVs as end nodes. To address this challenge, we introduce an air-ground model collaboration framework that considers heterogeneous computational resources among multiple UAVs and limited wireless transmission bandwidth between UAVs and ground servers. In this framework, UAVs working as end nodes handle data collection and local inference with small models, while the edge servers on the ground perform large model inference and updates. We propose a joint optimization strategy that optimizes both data transmission and resource allocation, with the goal of improving the framework's inference accuracy by maximizing the mean average precision (mAP). Simulations on object detection tasks show that our framework outperforms existing methods under different communication bandwidths and data scales.