Currently, the execution of machine learning algorithms is typically batch, offline and centralized. Network management and its services require massive execution of distributed and real-time data. In many situations, the real-time validity of the generated data is limited, requiring the reduction of latency in communication and processing. Furthermore, data transmission in a distributed environment is subject to the quality of communication channels, network congestion and available energy in mobile devices. Such restrictions require solutions based on Distributed Artificial Intelligence (DAI), which goes far beyond the traditional execution of machine learning algorithms. An additional strong restriction arises from the adoption of the new General Data Protection Law – LGPD. The data privacy restriction is addressed by the federated learning technique. The large number of devices connected to the Internet of Things requires the handling of a high volume of data generated by thousands of sensors, requiring solutions that meet scalability, geographic distribution, mobility, heterogeneity, security and privacy requirements.