Distributed Machine Learning Using Data Parallelism on Mobile Platform

Keywords: machine learning, distribution, data parallelism, mobile, client-server architecture, web service


Machine learning has many challenges, and one of them is to deal with large datasets, because the size of them grows continuously year by year. One solution to this problem is data parallelism. This paper investigates the expansion of data parallelism to mobile, which became the most popular platform. Special client-server architecture was created for this purpose. The software implementation of this problem measures the mobile devices training capabilities and the efficiency of the whole system. The results show that doing distributed training on mobile cluster is possible and safe, but its performance depends on the algorithm’s implementation.


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Author Biography

Máté Szabó, Department of Information Technology, Faculty of Informatics University of Debrecen, Kassai út 26, H-4028 Debrecen, Hungary

Máté Szabó received his MSc degree in computer science from the University of Debrecen in 2018. His research interests are machine learning, web services and distributed systems. He worked as a freelancer web application developer, but currently he is PhD student and lecturer at the University of Debrecen.


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