Prototype of deployment of Federated Learning with IoT devices

• URL: http://arxiv.org/abs/2311.14401v1
• Date: Fri, 24 Nov 2023 10:37:30 GMT
• Title: Prototype of deployment of Federated Learning with IoT devices
• Authors: Pablo Garc\'ia Santaclara and Ana Fern\'andez Vilas and Rebeca P. D\'iaz Redondo
• Abstract summary: A huge amount of the resource desired, data, is stored in mobile devices, sensors and other Internet of Things (IoT) devices. At the same time these devices do not have enough data or computational capacity to train good models. Federated Learning (FL) provides an innovative solution that allows devices to learn in a collaborative way.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the age of technology, data is an increasingly important resource. This importance is growing in the field of Artificial Intelligence (AI), where sub fields such as Machine Learning (ML) need more and more data to achieve better results. Internet of Things (IoT) is the connection of sensors and smart objects to collect and exchange data, in addition to achieving many other tasks. A huge amount of the resource desired, data, is stored in mobile devices, sensors and other Internet of Things (IoT) devices, but remains there due to data protection restrictions. At the same time these devices do not have enough data or computational capacity to train good models. Moreover, transmitting, storing and processing all this data on a centralised server is problematic. Federated Learning (FL) provides an innovative solution that allows devices to learn in a collaborative way. More importantly, it accomplishes this without violating data protection laws. FL is currently growing, and there are several solutions that implement it. This article presents a prototype of a FL solution where the IoT devices used were raspberry pi boards. The results compare the performance of a solution of this type with those obtained in traditional approaches. In addition, the FL solution performance was tested in a hostile environment. A convolutional neural network (CNN) and a image data set were used. The results show the feasibility and usability of these techniques, although in many cases they do not reach the performance of traditional approaches.

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