The Internet of Things (IoT) is becoming a part of everyday life through its various sensing devices that collect valuable information. The huge number of interconnected heterogeneous IoT devices poses immense challenges, and network softwarization techniques are an adequate solution to these concerns. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two key softwarization techniques that enable the realization of efficient, agile IoT networks, especially when combined with Machine Learning (ML), mainly Federated Learning (FL). Unfortunately, existing solutions do not take advantage of such a combination to strengthen IoT networks in terms of efficiency and scalability. In this paper, we propose a novel architecture to achieve distributed intelligent network softwarization for IoT, in which SDN, NFV, and ML combine forces to enhance IoT constrained networks.

翻译：物联网（IoT）正通过其各类传感设备收集有价值的信息，逐渐成为日常生活的一部分。大量异构物联网设备的互联互通带来了巨大挑战，而网络软化技术正是应对这些问题的有效方案。软件定义网络（SDN）和网络功能虚拟化（NFV）是两项关键的软化技术，能够实现高效、敏捷的物联网网络，尤其是在与机器学习（ML）（尤其是联邦学习（FL））结合时。遗憾的是，现有解决方案并未充分利用这种组合来增强物联网网络的效率和可扩展性。本文提出了一种新颖的架构，旨在实现面向物联网的分布式智能网络软化，其中SDN、NFV与ML协同作用，以增强受限物联网网络的性能。