Analysis of Resource Usage Management Plan for Federated Learning in Hybrid Cloud

Sangwon Oh, Hyeju Shin, Minsoo Hahn, Jinsul Kim
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Abstract

With the emergence of a flexible mix of private and public clouds based on business requirements, the need for a system that supports application deployment to a variety of cloud environments has emerged. In particular, it is necessary to secure the security of data in applications based on federated learning and to monitor resource usage in the cloud. This paper seeks ways to monitor and manage cloud resource usage according to various hyperparameters when conducting federated learning in a hybrid cloud environment. In a Docker-based cloud environment, we present an improved method for using efficient cloud resources while controlling the metric and resource usage trend of the federated learning model according to the imbalance of the data set.
混合云中联邦学习的资源使用管理方案分析
随着基于业务需求的私有云和公共云的灵活组合的出现,对支持将应用程序部署到各种云环境的系统的需求已经出现。特别是,有必要确保基于联邦学习的应用程序中的数据安全性,并监控云中的资源使用情况。本文寻求在混合云环境中进行联邦学习时,根据各种超参数监控和管理云资源使用情况的方法。在基于docker的云环境中,我们提出了一种改进的方法,在有效利用云资源的同时,根据数据集的不平衡性控制联邦学习模型的度量和资源使用趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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