## 2 Related Work
### Machine Learning-Based Approach for Resource Scheduling  
With the development of the machine learning approach, utilizing machine learning to solve existing problems becomes an effective solution. A Deep Reinforcement Learning-based service-oriented resource allocation algorithm is proposed ([PERSON] et al., 2018). [PERSON] et al. (2018) proposed a reinforcement learning approach to optimize scheduling games in mobile edge computing. Their method considers both the quality of mobile device-server links and the allocation of server computing resources. A resource allocation algorithm is presented in ([PERSON] et al., 2018). The algorithm combines Deep Reinforcement Learning and Genetic Algorithm to solve problems in mobile edge networks, including transmission power allocation and computation resources allocation. In ([PERSON] et al., 2018), the authors investigate the task scheduling and resource allocation problem in edge computing scenarios, with the objective of maximizing the long-term satisfaction of tasks allocated to virtual machines deployed on edge servers. Markov decision process is formulated as to model to deal with problems, which involves states, actions, state transitions, and rewards. Following that, a resource allocation algorithm based on Deep Q Neural Network is designed to determine an optimal offloading decision and resource allocation scheme. In addition to utilizing AI to achieve efficient resource allocation, the execution of AI business also involves the scheduling of computing power resources. Traditional centralized AI models cannot fully utilize edge network computing power. Distributed model training has been a topic of significant interest in recent years, with various studies focusing on improving the efficiency and scalability of training models across distributed systems. Machine learning has a wide range of practical application scenarios in edge computing's first network-enabled smart grid ([PERSON] et al., 2018). A DRL-based edge computing network-aided resource allocation algorithm was proposed for the smart grid ([PERSON] et al., 2018). [PERSON]. [PERSON] et al. investigated the edge computing strategies for smart grids. A comprehensive review of emerging issues and edge computing in a smart grid environment is discussed and explained ([PERSON] et al., 2018). An architecture of edge computing-enabled Internet of Thing (IoT)-based smart grid is proposed. Moreover, three major scenarios of power systems, the power distribution, Micro-grid, advanced metering systems, and application of edge computing, are well-represented ([PERSON] et al., 2018). However, existing computing resource scheduling algorithms cannot solve the efficient distribution of AI services characterized by diversified types and time variability to edge data centers with changing workloads in the ECFN-enabled smart grid. Some research studies have investigated distributed model training based on data parallelism in edge computing networks. However, the optimal goals of existing studies mainly focus on how to minimize communication bandwidth for distributed training [24] and jointly optimize the training time and energy consumption [25]. The adaptive DNN model partition and deployment are investigated in edge computing-enabled metro optical interconnection networks [26]. [PERSON] et al. [27] optimized the partitioning and distribution of training data within edge computing-enabled optical networks to enhance the efficiency of distributed model training services. [PERSON] et al. [28] introduced Pufferfish, a distributed training framework designed for communication and computation efficiency. It integrates gradient compression techniques into the model training process. [PERSON] et al. [29] investigated federated learning aggregation strategies, such as FedAvg, to derive a comprehensive \"average\" model from models trained across distributed clients. Overall, the research in the above papers only considers the efficiency of training models and the performance of the models.