  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.
