## 2 Related Works  
Previous research was presented by [PERSON] et al. which focuses on optimizing the methods for offloading computations in edge computing networks [17]. More specifically, the authors studied six different optimization methods: convex optimization, Lyapunov optimization, heuristic techniques, machine learning, and game theory method. For each of these methods, the authors focused on the objective functions (minimizing energy consumption, minimizing latency, and minimizing the system utility and the system cost), the application areas, for example, MEC or MCC, the types of offloading methods, and the evaluation methods (simulation or real-world dataset). To conclude, this paper provided a summary of some optimization methods for offloading computations in edge computing networks which can support scholars in their computational offloading research. A comprehensive survey that provides a thorough examination of computational offloading in MEC, covering various objectives such as minimizing delay, reducing energy consumption, maximizing revenue, maximizing system utility, applications, and offloading approaches, was presented by [PERSON] et al. [18]. In more detail, the authors conducted a thorough review of the benefits of computation offloading techniques in MEC networks. They analyzed existing works from various perspectives to categorize them based on the application offloading objectives. The authors provide an analytical comparison of various computation offloading techniques, including mathematical solvers, heuristic algorithms, Lyapunov optimization, game theory, the Markov Decision Process, and Reinforcement Learning. They discuss the advantages and disadvantages of each method. Additionally, the paper concludes by highlighting the different challenges faced in computation offloading within MEC networks which must be studied and researched. Some authors like [PERSON] et al. [19] have also provided a comprehensive survey of edge computing and deep learning. This survey first discusses the fundamentals of this technology, giving some paradigms of edge computing. The concepts discussed are the Cloudlet and micro data centers, mobile edge computing (MEC), fog computing, and collaborative end-edge-cloud-computing. In addition, potential hardware chips for edge AI applications are described. Lastly, the researchers discuss the challenges of the possibility of DL applications being executed on edge devices, focusing on inference, training, and optimization. The contribution of this paper is the provision of insights on how to identify the most suitable edge computing architecture that can optimize deep learning performance for both training and inference and also address challenges like energy consumption, latency, and network communications. Another promising line of research was introduced by [PERSON] et al. [20] which emphasizes the difficulties and solutions of communications in edge AI. The main information that this work is providing is a summary of the communications algorithms for distributed training of various AI models that can be deployed on edge nodes, such as zeroth-order, first-order, second-order, and federated optimization algorithms. Additionally, the authors aim to classify different system architectures of an edge AI system, including partition-based and model partition-based edge training systems. In addition, this paper reviews works based on computation offloading and inferencing at the edge of the network. Finally, different edge AI systems were introduced with discussions focusing on the challenges and solutions of the communications in these systems. The implementation of deep learning algorithms in edge computing has garnered increasing attention in recent years. [PERSON] et al. in [21] presented an analytical review that provides an overview of deep learning applications like computer vision, natural language processing, augmented reality, etc. which are used in edge computing.