【摘 要】
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In recent years,mobile edge computing has attracted a considerable amount of attention from both aca-demia and industry through its many advantages(such as low latency,computation efficiency and pri-vacy) caused by its local model of providing storage and
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In recent years,mobile edge computing has attracted a considerable amount of attention from both aca-demia and industry through its many advantages(such as low latency,computation efficiency and pri-vacy) caused by its local model of providing storage and computation resources.In addition,machine learning has become the dominant approach in appli-cations such as industry,healthcare,smart home,and transportation.All of these applications heavily rely on technologies that can be deployed at the network edge.Therefore,it is essential to combine machine learning with mobile edge computing to further pro-mote the proliferation of intelligent edges.In gener-al,machine learning relies on powerful computation and storage resources for superior performance,while mobile edge computing typically provides lim-ited computation resources locally.To this end,the implementations of machine learning algorithms should be revisited for mobile edge computing.
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