Distributed Training of ML Model on Mobile Devices

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Published: 23.08.2020
UDC 004.41 004.02
DOI: https://doi.org/10.26907/1562-5419-2020-23-5-1076-1092
Issue
Vol. 23 No. 5 (2020)

Main Article Content

Denis Vasilyevich Simon
Higher Institute of Information Technology and Intelligent Systems, Kazan Federal University
Irina Sergeevna Shakhova
Higher Institute of Information Technology and Intelligent Systems, Kazan Federal University

Abstract

Currently, the need for distributed ML training solutions in the world is increasing. However, existing tools, in particular TensorFlow Federated, are at the very beginning of their development, difficult to implement, and currently suitable exclusively for simulation on servers. For mobile devices, reliable approaches for this purpose do not exist. This article has designed and presented an approach to such distributed training of the ML-model on mobile devices, implemented on existing technologies. It is based on the concept of model personalization. In this approach, this concept is improved as a consequence of mitigating the identified drawbacks. The implementation process is structured so that at all stages of working with the ML-model use only one Swift programming language (Swift for TensorFlow and Core ML 3 are used), making this approach even more convenient and reliable due to the common code base.

Article Details

Author Biographies

Denis Vasilyevich Simon

Postgraduate student of the Higher School of Information Technologies and Intelligent Systems at Kazan Federal University, iOS developer.

Irina Sergeevna Shakhova

Senior teacher of the Higher School of Information Technologies and Intelligent Systems at Kazan Federal University. Research interests include digital educational systems, individualization of education, mobile learning.

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