 Corresponding author: Barida Baah Department of Computer Science, Ebonyi State University, Abakaliki- Nigeria. 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. 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. Global Journal of Engineering and Technology Advances, 2021, 06(03), 063–068 Publication history: Received on 01 February 2021; revised on 05 March 2021; accepted on 08 March 2021 Article DOI: https://doi.org/10.30574/gjeta.2021.6.3.0042 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. Keyword: Wireless; Federated Machine Learning; Raspberry Pi 1. Introduction Applied learning strategies include integrating training data into a specified database or database. For example, if the first e-commerce company wants to set up its customer information module for purchasing its products, it would run the information on the data collected from the application or website. The data may be related to the time spent on the actual product page Products that have been searched but not purchased and products that have been purchased. The data should be sent and received through an encoder or data source [1]. Although the comparisons could be quite simple, computer history could substitute for what Federated Learning is all about. There has been a huge gap in the early days of information technology, making the most advanced computer simulations. Finally, the researcher switched to a remote system where computers were distributed between client computers, clients, and internal servers [2]. The Federated Learning structure uses the same model. Machine learning machines are distributed over the systems of computer equipment, rather than on large, centralized systems. This computer model, while initially thought of, would not have been hand-made, as the mobile computer would be too slow to run any Machine Learning module. Consequently, learning improved in the near-to-late 2018s. Since billions of downloads, equipped with AI chips and high-capacity computers, starting with the Samsung S9, or Apple X series, some of the machine learning(ML) models should run in that and focus on such phones, delivered in the next 3-5 years.