## 2 Related Work
### Algorithms  
In terms of algorithms, DRL emerged from the work of [PERSON] et al. [34], who combined convolutional neural networks with the Q-learning algorithm from traditional reinforcement learning to propose the Deep Q-Network (DQN) model. The DQN model was initially applied to visual-perception-based control tasks, representing a ground-breaking achievement in DRL. Building upon this idea, [PERSON] et al. [15; 35] expanded the Q-learning algorithm using the concept of DQN and introduced the DDPG algorithm, which combines value and policy gradients to handle the DRL problem in continuous action spaces. On the other hand, the FL algorithm enables collaborative model training without the need for data sharing [16]. In this approach, each device possessing local data keeps its data locally and exchanges undisclosed parameters with other devices to create a shared global model within the FL system, employing techniques such as federated averaging [36]. The processed global model is then distributed to each device and exclusively used for local model training [37]. [PERSON] et al. [38; 39] introduced a hybrid framework that combines FL, artificial bee colony optimization, and support vector machines. This framework aims to achieve optimal feature selection and classification of diseases on the client side of health service providers, effectively addressing privacy concerns.  
FL is beneficial in reducing convergence problems, improving algorithm performance, and ensuring data privacy. Regarding the integration of DRL and FL, [PERSON] et al. [40] proposed a novel two-timescale DRL approach that utilizes FL for distributed training of the aforementioned model. The primary objective is to minimize overall offload latency and network resource utilization by jointly optimizing computation offloading, resource allocation, and service cache placement, all while safeguarding data privacy for edge devices. In a similar vein, [PERSON] et al. [41] developed a method that leverages deep Q-learning to enhance the efficiency of FL algorithms and reduce the number of communication rounds required for FL. In another work, [PERSON] et al. [42] devised a framework based on FL and reinforcement learning. Multiple local models collaborate to learn a global model, and its convergence is analyzed. Lastly, [PERSON] et al. [43] proposed a federated reinforcement learning approach that combines the privacy protection offered by FL with the problem-solving capabilities of reinforcement learning. Their method introduces increased concurrency to enhance the learning process. [PERSON] et al. [44] introduce a layered framework for federated edge learning that migrates the model aggregation part from the cloud to the edge server and offers a productive resource scheduling technique that will reduce the overall cost. In order to reduce compute offload latency and resource usage while maintaining data privacy for edge devices, [PERSON] et al. [45] developed a dual-timescale technique employingdistributed FL training. For AC-based FL, [PERSON] et al. [46] introduced an online approach. The model is trained by each edge node in its MEC system, and a weighted loss function is used to facilitate model optimization.  
The above is the related work of our article. At present, academic research on edge collaborative computing offloading and resource allocation of MEC networks has been extensively carried out. However, for a smart factory in an Industrial Internet scenario, the research on multiedge-terminal collaborative computing offloading and resource allocation is not enough. Recognizing this research gap, we initiated an in-depth study to address this crucial area of investigation.