# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 5 Conclusions and Future Work  
This study proposes a cloud-edge collaborative model adaptation method that combines DQN-based decision making with transfer feature extraction, aiming to enhance the consistency between edge-side inference results and cloud-side fusion results while ensuring the robustness of the edge model. The proposed method validates the feasibility and effectiveness of a DQN-based local migration strategy in ensuring the consistency between edge-side inference results and cloud-side fusion results, while also maintaining the robustness of the edge detection model to a certain extent. However, due to the inherent limitations of the edge model's detection accuracy, the current feature migration strategy still struggles to significantly overcome performance bottlenecks in category classification tasks. In addition, the proposed method still has certain limitations in handling edge intelligence tasks, such as few-shot learning, new category adaptation, and long-term online updates. On the one hand, the local migration strategy relies on sufficient training samples of existing categories, making it less adaptable to new categories or scenarios with scarce data. On the other hand, as edge devices operate over extended periods, the number of target categories will continuously expand. Therefore, the model needs to possess continuous learning capabilities to prevent previously learned knowledge from being overwritten by new tasks, thus avoiding catastrophic forgetting. Based on the above issues, future research will focus on the following three directions to further improve the cloud-edge collaborative optimization framework. (1) Enhancing the Detection Accuracy of the Edge Model Itself  
In the future, the detection capability of the edge model will be comprehensively improved by enhancing the diversity of training data, such as expanding data sampling and applying data augmentation strategies, and by optimizing the model's architecture, for example, by introducing lightweight and efficient feature extraction modules and multi-scale detection heads. These enhancements will strengthen the model's perception accuracy from the source, providing a more solid performance foundation for subsequent migration and updates. (2) Introducing Meta-Learning Mechanisms to Enhance Few-Shot Adaptation Capability  
The current strategy mainly focuses on migration optimization for known categories, lacking the ability to quickly adapt to new classes. Future research will introduce a meta-learning framework, such as MAML or Prototypical Networks, for pre-training on the cloud side. This will enable the edge model to rapidly learn from a small number of samples, effectively addressing few-shot scenarios, such as the emergence of new aircraft types in real-world applications, and enhancing the model's generalization capability. (3) Integrating Incremental Learning Strategies to Achieve Continuous Learning and Knowledge Retention  
To address the practical need for continuously expanding target categories in edge scenarios, it is necessary to construct an update mechanism with knowledge retention. Future work will explore class-incremental learning methods, such as Elastic Weight Consolidation (EWC), Learning without Forgetting (LwF), and replay buffer strategies, to enable the model to retain the recognition ability of previously learned categories when incorporating new ones. This will help prevent catastrophic forgetting and support the long-term stable operation and knowledge-compatible learning of edge intelligence systems. Conceptualization, S.T. ; Methodology, J.C., X.C. and Y.J. ; Validation, J.C.; Formal analysis, S.T. ; Investigation, Y.J. ; Writing--original draft, J.C.; Visualization, Y.J. ; Supervision, X.C. and S.T. ; Funding acquisition, X.C. and S.T. All authors have read and agreed to the published version of the manuscript.