Two-Tier Submodel Partition Framework for Enhancing UAV Swarm Robustness in Forest Fire Detection

The deployment of Unmanned Aerial Vehicle (UAV) swarm for Forest Fire Detection (FFD) missions presents unique challenges, e.g., the early forest fires are difficult to identify due to environment diversity and feature complexity, especially when some UAVs could be destroyed in harsh environments. To address these challenges, UAV swarm-based FFD missions can leverage advanced deep learning techniques, where online model updates, robustness, and communication overhead control become crucial for ensuring the effectiveness and adaptability of these missions. In this paper, we propose a Two-tier Submodel Partition Framework (TSPF) to enhance the robustness of UAV swarm conducting FFD missions. TSPF utilizes online model updates to adapt to diverse mission environments, thus strengthening the generalization capability of the model. In addition, a graph coloring method, an intragroup backup mechanism, and a Dynamic Server Selection (DSS) mechanism for the grouping are employed to enhance the robustness of FFD missions when some UAVs are destroyed, hence maintaining the high performance of FFD missions in harsh environments. Moreover, TSPF enables submodel updates by aggregating the parameters of selected layers within/between UAV groups, thereby effectively reducing the model parameter uploads (communication overhead) in model training. Experimental evaluations demonstrate that our proposed TSPF significantly improves the detection accuracy of forest fires, enhances the robustness of FFD missions against the destruction of some UAVs, and reduces the communication overhead in FFD missions.