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.

翻译：在技术时代，数据日益成为重要资源。其在人工智能（AI）领域的重要性与日俱增，其中机器学习（ML）等子领域需要更多数据以获得更优结果。物联网（IoT）通过连接传感器与智能设备实现数据采集与交换，同时完成诸多其他任务。海量数据资源存储于移动设备、传感器等物联网设备中，却因数据保护限制留存原地。然而这些设备缺乏充分数据或计算能力以训练优质模型。此外，将所有数据传输、存储并集中于中央服务器处理存在诸多问题。联邦学习（FL）提供创新解决方案，使设备能够以协作方式学习。更重要的是，该方法在不违反数据保护法规的前提下实现目标。联邦学习目前发展迅速，已有多种解决方案落地实施。本文提出一种FL解决方案原型，实验采用树莓派板卡作为物联网设备。结果对比了此类方案与传统方法的性能差异。此外，在对抗性环境下测试了FL方案的性能表现。实验采用卷积神经网络（CNN）与图像数据集。结果表明，尽管多数情况下这些技术未达到传统方法的性能水平，但验证了其可行性与实用性。