It is a fact that Artificial Intelligence is having an ever-growing impact on society. That is not just because of advances in computational power and in machine learning models, such as deep neural networks, but also because of the availability of a large volume of heterogeneous data from diverse sources. The Internet of Things (IoT) paradigm is helping gather massive amounts of data from sensor networks that can be used to train and generate complex AI models. However, the training of these models needs not only the data but has high computational requirements. In this scenario there has appeared a new paradigm, called the Internet of Agents (IoA), which allows the inclusion of intelligence and autonomy in IoT devices and networks. This paper presents an IoA architecture that allows the continual and distributed generation and exploitation of convolutional neural networks. Specific protocols for the safe and efficient transmission of models and training pictures are designed. The convolutional model is trained in the cloud and, once reduced, it is distributed and executed in agents located in embedded devices with low computational resources. The architecture has been tested using a convolutional model for the recognition of handwritten character digits based on the MNIST database.

事实是，人工智能对社会的影响越来越大。这不仅是因为计算能力和机器学习模型（例如深度神经网络）的进步，还因为可获得来自各种来源的大量异构数据。物联网（IoT）范例正在帮助从传感器网络收集大量数据，这些数据可用于训练和生成复杂的AI模型。但是，这些模型的训练不仅需要数据，而且对计算的要求很高。在这种情况下，出现了一种新的模式，称为代理程序Internet（IoA），该模式允许将智能和自治功能纳入IoT设备和网络。本文提出了一种IoA架构，该架构允许对卷积神经网络进行连续和分布式的生成和利用。设计了用于安全有效传输模型和训练图的特定协议。卷积模型是在云中训练的，一旦减少，它就会在计算资源较低的嵌入式设备中的代理中分发和执行。已使用卷积模型对该体系结构进行了测试，以基于MNIST数据库识别手写字符数字。它在计算资源较低的嵌入式设备中的代理中分发和执行。已使用卷积模型对该体系结构进行了测试，以基于MNIST数据库识别手写字符数字。它在计算资源较低的嵌入式设备中的代理中分发和执行。已使用卷积模型对该体系结构进行了测试，以基于MNIST数据库识别手写字符数字。