  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.
