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Smart grid applications, such as predicting energy consumption, grid user behavior analysis and predicting energy theft, etc., are data-driven applications that require machine learning with a wealth of data generated from Internet of Things (IoT) based metering devices. However, traditional methods of uploading this huge data to the remote cloud for data analytics may be low efficient due to the non-negligible network transmission delay. By deploying a number of computing-enabled devices at the network edge, edge computing supports the implementation of machine learning close to the power grid environment. Considering the limited computing resources of edge devices and non-independent and identical (non-IID) data source, federated learning is a feasible edge computing based machine learning model. In federated learning, distributed mobile clients and a federated server collaborate to perform machine learning. Generally, the more clients to join the federated learning, the faster to obtain learning convergence and the higher resource utility. However, the communications between clients and the server in training rounds of federated learning may fail due to time-varying link reliability properties in a wireless network of smart grid, which not only slows down the model convergence rate but also wastes resources, such as energy consumption for invalid local training. This paper studies a dynamic federated learning problem in a power grid mobile edge computing (GMEC) environment, considering the high dynamic of link reliability. We design a delay deadline constrained federated learning framework to avoid extremely long training delay, and then formulate a dynamic client selection problem for computing utility maximization in such learning framework. Two online client selection algorithms, including climax greedy and uti-positive guarantee, are proposed to address the problem. The theoretical analysis and simulation results are conducted to illustrate the efficiency of the proposal. INDEX TERMS Mobile edge computing, machine learning, federated learning, smart grid, link reliability.

In recent years, the frequent occurrence of food safety incidents has brought great impact on consumers' confidence in food safety, which has caused the food problem to become an important livelihood issue that needs to be improved and solved. The essence of food safety incidents is the market failure caused by the asymmetry of food safety information. Therefore, by taking the food safety as object of study, this paper firstly adopts the questionnaire research method to investigate the consumer's food safety risk perception. Besides, according to the theories such as consumer behaviour decision-making and asymmetric information etc., the model of consumer decision-making behaviour based on a rational model was constructed, and the factors that affect consumer decision-making behaviour were studied. The results show that consumers are more concerned about the safety issues of food quality, but the satisfaction of the government's supervision and food producer' control on food quality is not high; consumers' information recognition of food safety, risk perception, and consumer's expectation all will directly or indirectly affect consumer decision-making behaviour. Finally, it proposes the suggestions and countermeasures for improving China's food safety supervision as well as strengthening consumers' food safety awareness, which is expected to help reduce consumer perception of food safety risks and restore their confidence in food safety.

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With the rapid development of the global information technology and the widespread application, enterprises put higher forward requirements on the implementation of ERP performance management. In order to solve the problem that majority of enterprises performance evaluation system of ERP attach importance to financial index system, light the future value creation, pay attention to performance of tangible assets, light intangible assets value of light, think highly of result, light process and other issues, this paper absorb and digest theoretical research about the Balanced Scorecard combined with system dynamics. In view of these, this paper aims to provide a dynamic balanced scorecard based on the performance evaluation model of ERP implementation. Then, for enterprises implemented ERP as an example, this paper establishes the dynamic model of ERP performance evaluation, which uses Vensim software and combines the status of enterprises. Also, this thesis mainly put this dynamic model into empirical analysis. Finally, according to the empirical analysis, we can draw the conclusion that the system dynamics not only can effectively make up for the lack of balance score card, such as the time lag and lack of system feedback, but also can prove the effectiveness of information dynamic evaluation model, which can provide support and reference for decision-making and performance management, avoiding subjective thinking defect of managers.