摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-关于人工智能的详细数据已经呈现。据新闻报道NewsRx记者从中华人民共和国北京报道，研究称，“抽象”新闻记者从北京理工学院的研究中获得了一句话：“在这种情况下，”分布式机器学习(distributed machine learning,D ML)是机器学习领域中的一个研究热点并广泛应用于交通、医药、商业等领域。分布的机器学习在处理海量数据的同时保持数据的私密性方面显示出巨大的潜力。本文介绍了一个分布式的深度神经网络(DNN)的实现,该网络使用了一个大的数据集以某电信公司为研究对象，基于用户使用数据进行用户行为预测。各种具有Distin CT异质性设置的数据集被应用到网络中，模拟真实世界的C loud应用程序场景。对集中式模型和分布式模型进行了比较分析了数据异构性和数据异构性对模型性能的影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Data detailed on artificial intelligen ce have been presented. According to newsreporting originating from Beijing, Pe ople’s Republic of China, by NewsRx correspondents, research stated,“Abstract.”The news journalists obtained a quote from the research from Beijing Institute o f Technology: “In thefield of machine learning, distributed machine learning (D ML) has gained massive popularity in researchand appeared in a wide range of ap plications including transportation, medicine, and business. Distributedmachine learning showed huge potential in processing large amounts of data while keepin g the data private.In this paper, a distributed implementation of a deep neural network (DNN) with a large dataset froma telecom company, aiming to predict co nsumer behavior based on usage data is carried out. Variousdatasets with distin ct heterogeneity settings were applied to the network, simulating a real-world c loudapplication scenario. Comparisons between both the centralized model and th e distributed models weremade to analyze the impact on model performance with d evice heterogeneity and data heterogeneity.”
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