# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
###### Abstract  
With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. caption =1  
Footnote †: journal: Applied sciences  
intelligent analysis. The collaboration between cloud computing and edge computing has become the mainstream architecture. Edge computing is responsible for real-time data processing at the device level near the data source, while cloud computing handles large-scale data analysis, storage, and processing in the resource-rich cloud environment [1]. Reasonably, allocating tasks between the cloud and the edge to achieve cloud-edge collaborative decision making has become a mainstream research focus in recent years. Cloud-edge collaborative decision making features distributed processing and low latency [2], making it widely applicable in fields like intelligent security, industrial quality inspection, autonomous driving, and smart transportation [3]. Although the cloud-edge collaborative architecture provides strong support for various intelligent application scenarios, the strict constraints on computing power, energy consumption, and storage of edge devices lead to reduced accuracy and robustness of models in tasks like object detection or classification [4]. To this end, a growing number of studies have focused on the fine-tuning of edge-side models (see [5; 6]) by introducing an optimization mechanism equipped with intelligent perception and decision making capabilities. This enables the system to adaptively perform localized updates of the classification head based on the real-time state of edge devices, thereby improving the long-term stability and adaptability of edge-side models. To address the need for local adaptation of edge models in classification tasks within object detection, RL (reinforcement learning) offers a feasible strategy-driven framework capable of learning optimal action policies through interaction with environmental feedback [7]. In particular, the Deep Q-Network (DQN) models the decision making process through a state-action-reward framework and can be employed to intelligently determine whether model transfer should be performed for a specific target class [8].