  Implementing existing federated learning in massive Internet of Things (IoT)
networks faces critical challenges such as imbalanced and statistically
heterogeneous data and device diversity. To this end, we propose a
semi-federated learning (SemiFL) framework to provide a potential solution for
the realization of intelligent IoT. By seamlessly integrating the centralized
and federated paradigms, our SemiFL framework shows high scalability in terms
of the number of IoT devices even in the presence of computing-limited sensors.
Furthermore, compared to traditional learning approaches, the proposed SemiFL
can make better use of distributed data and computing resources, due to the
collaborative model training between the edge server and local devices.
Simulation results show the effectiveness of our SemiFL framework for massive
IoT networks. The code can be found at https://github.com/niwanli/SemiFL_IoT.
