## 1 Introduction  
In recent decades, unmanned aerial vehicles (UAVs) have been employed in various applications [1, 2, 3, 4, 5, 6, 7] thanks to their flexibility, maneuverability, wide coverage, and real-time data collection [8]. When equipped with various sensors, UAVs can monitor target objects and transmit information in real-time during missions [9]. In the initial stages of UAV applications, missions were conducted with a single UAV, but this approach may be challenging to apply to large-scale operations, as the malfunction of a single UAV could result in the abortion of the mission. Unlike a single UAV, a UAV swarm can provide several advantages, such as reducing operation time, decreasing mission failure rate, and multitasking capability through cooperation and parallel processing [10, 11]. Furthermore, the UAV swarm is suitable for large-scale operations due to their wide coverage and enhanced cost-effectiveness [12, 13, 14]. The use of convolutional neural networks (CNNs) in a variety of UAV missions has been the main objective of numerous studies [15, 16]. DNN architectures have become more complicated as UAV missions in a complex plan become more demanding. Naturally, the memory usage and computational requirements of models have increased, which may pose challenges when deploying CNN models on resource-constrained UAVs [17]. Severalsolutions have been proposed to address the discrepancy between the substantial resource requirements of CNN models and the restricted resource supply of UAVs. Cloud-based intelligence has addressed these challenges by transferring raw UAV data to a cloud server for CNN inference [18; 19; 20]. This approach offers computing resources from powerful cloud servers. However, it can suffer from high transmission latency due to the long distance between the UAV and the cloud server. Moreover, unstable network conditions in battlefield or disaster areas can disrupt data transmission, thereby limiting the feasibility of cloud-based intelligence. Edge-based intelligence has implemented CNN inference on edge servers near UAVs to circumvent the resource requirements of CNNs [21; 22; 23]. However, this approach may constrain the mobility of UAV swarms because they need to remain within the operational range of edge servers to maintain connectivity. Additionally, server overload may occur when multiple UAVs simultaneously offload to a single edge server. To address problems related to transmission latency and UAV mobility constraints, recent works [24; 25; 26] have focused on jointly executing inference for complex CNN models on resource-constrained devices without relying on cloud or edge servers. This approach, called device-device collaborative edge intelligence (CoEI), performs distributed inference by partitioning the CNN model into segments (e.g., layers, multiplication tasks) and assigning the segments to devices. The device-device CoEI is suitable for swarms consisting of multiple UAVs and enables CNN inference through the collective power of the swarm, even when the resources of UAVs are limited. In [24], a pipeline-based collaborative method has been proposed to conduct CNN inference on a resource-constrained UAV swarm. However, this method was tested with a small number of UAVs, which can lead to concerns about scalability in a larger UAV swarm. In [25], an optimal UAV-based layer distribution method was proposed to minimize the end-to-end latency of the distributed inference by allocating partitioned CNN layers. However, this method does not consider UAV-to-ground (U2G) communication link needed to transmit results from the UAV network to ground devices. Incorporating the U2G link can more effectively optimize overall end-to-end latency. [PERSON] et al. [26] proposed a method for distributed inference by optimizing layer assignment using deep reinforcement learning (DRL). However, this method may overlook scenarios where a UAV becomes inoperable due to internal or external factors. The absence of a resilience mechanism for inoperable UAVs may lead to mission failure.