# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
###### Abstract
In addition, the feature extraction mechanism in transfer learning [9] can adjust the feature parameters of a specific category in the edge model based on discrepancies between detection results from the cloud and the edge. This enables precise and cost-effective model enhancement without compromising the stability of the edge-side backbone network. The integration of both approaches gives rise to a collaborative optimization framework that unifies cloud-edge policy decision making with parameter transfer. The DQN agent makes transfer decisions for specific categories based on the observed performance discrepancies between the cloud and edge environments. The transfer mechanism achieves rapid adaptation to edge-side data by replacing the parameters of the classification head. This design not only enhances the robustness of the edge model in complex and dynamic scenarios but also facilitates efficient coordination of computational resources between the cloud and the edge. In recent years, various approaches have been proposed to address model deployment and optimization in edge environments, including lightweight techniques, such as model pruning, quantization, distillation, and parameter sharing. Pruning (see [10; 11]) and quantization (see [12; 13]) reduce model storage and improve inference efficiency by removing unimportant connections or neurons in neural networks or by converting high-precision floating-point values into low-precision integers. Distillation (see [14; 15]) reduces model size while preserving performance by transferring knowledge from a large model to a smaller one. Parameter sharing (see [16; 17]) reduces the number of parameters and improves computational efficiency by reusing identical weights across the network. Although these methods effectively reduce model size and computational overhead, they still require a trade-off between accuracy and efficiency in practical applications. Moreover, existing studies often focus on compressing or distilling the entire model, while few approaches address localized transfer and adaptive control specifically targeting the classification head. Meanwhile, in edge environments characterized by significant data heterogeneity, there remains a lack of suitable transfer methods equipped with intelligent decision making mechanisms. While reinforcement learning has been explored in areas like task scheduling and inference path selection [18], research on class-wise decision making for local model transfer using DQN is still in its early stages [19]. Ref. [20] employed a Deep Q-Network (DQN) to perform collaborative scheduling among edge devices within a Multi-Access Edge Computing (MEC) architecture, aiming to solve the job shop scheduling problem in smart factory processes, while ref. [21] proposed a Prioritized Action Sampling-based Dueling DQN (PASD) algorithm to determine task offloading and resource allocation strategies, aiming to minimize average task delay and total system energy consumption, thereby addressing the joint allocation of network and computing resources in cloud-edge collaborative Industrial Internet of Things (IIoT) environments. This is particularly scarce within the context of cloud-edge collaboration. Therefore, there is an urgent need for a systematic approach that integrates reinforcement-learning-based decision making mechanisms with local transfer optimization strategies to enable adaptive edge model deployment. This paper addresses the challenges of the sky-ground integrated systems, specifically the limited edge computing resources and the inconsistency between cloud-edge fusion results and edge-side detection results. The paper proposes a cloud-edge collaborative edge model adaptation method that integrates DQN-based decision making with transfer feature extraction. The aim is to enhance the consistency between the edge model and the cloud fusion results, thereby ensuring the robustness of the edge model. The main contributions of this paper include the following.