A novel approach for federated machine learning using Raspberry Pi

Barida Baah 1, *, Onate Egerton Taylor 2 and Chioma Lizzy Nwagbo 3

1 Department of Computer Science, Ebonyi State University, Abakaliki- Nigeria.
2 Department of Computer Science, Rivers State University, Port Harcourt-Nigeria.
3 Department of Computer Science and Robotics Education, College of Education, Nsugbe, Anambra State, Nigeria.
 
Research Article
Global Journal of Engineering and Technology Advances, 2021, 06(03), 063-068.
Article DOI: 10.30574/gjeta.2021.6.3.0042
Publication history: 
Received on 01 February 2021; revised on 05 March 2021; accepted on 08 March 2021
 
Abstract: 
The problems of privacy and security is becoming a major challenge when it comes to the distributed systems, federated machine learning system especially when data are been transmitted or learned on a network , this necessitated the reasons for this research work which is all about wireless federated machine learning process using a Raspberry Pi. The Raspberry Pi 4 is a single hardware board with built in Linux operating system. We used data set of names from nine (9) different languages and then develop a training model using recurrent neural network to train this names compare to the names in the existing language like French, Scottish to predict if the names are from any of this language, this is done wirelessly with the Wi-Fi network in a federated machine learning environment for experimental setup with PySft’s that is installed in the python environment. The system was able to predict that name from which the language it originate from, the methodology that is implore in the research work is the Rapid Application Development (RAD). The benefits of this system are to ensure privacy, reduces the computing power, ensure real time learning and most importantly it is cost effective.
 
Keywords: 
Wireless; Federated Machine Learning; Raspberry Pi
 
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Copyright information: 
Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0