An opinion mining with federated learning on the Afghan-People survey dataset

M. Ahmadzai, Giang Nguyen
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Abstract

Internet and digital devices have caused a flood of information in the fourth industrial revolution era. Health-care, government, banks, military, and the banking sector, have embraced machine learning as a convenient way to recognize patterns in data. For a conventional machine learning model, the information from the data owner must be uploaded in a centralized location for training, which causes data owners to worry about the lack of their private information assurance. On the other hand, a massive amount of computing power is available to train intelligent models. In this way, federated learning becomes more and more popular. It preserves privacy of data owners and allows models to be trained over distributed clients. In this work, the federated learning approach is implemented using a neural network to model several Afghan-People opinion investigations such as the question about country's overall situation or people's security and safety in a distributed computing environment with measurable results. Further, the performance of federated learning using the same dataset is compared with centralized machine learning.
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基于阿富汗人调查数据集的联合学习意见挖掘
在第四次工业革命时代,互联网和数字设备带来了大量的信息。医疗保健、政府、银行、军队和银行部门已经接受了机器学习,将其作为识别数据模式的方便方法。对于传统的机器学习模型,来自数据所有者的信息必须上传到一个集中的位置进行训练,这使得数据所有者担心缺乏他们的私人信息保障。另一方面,大量的计算能力可用于训练智能模型。通过这种方式,联邦学习变得越来越流行。它保护了数据所有者的隐私,并允许在分布式客户端上训练模型。在这项工作中,使用神经网络实现了联邦学习方法,以模拟几个阿富汗人的意见调查,例如在分布式计算环境中关于国家总体情况或人民安全和安全的问题,并具有可测量的结果。此外,将使用相同数据集的联邦学习的性能与集中式机器学习的性能进行了比较。
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