  One key vision of intelligent Internet of Things (IoT) is to provide
connected intelligence for a large number of application scenarios, such as
self-driving cars, industrial manufacturing, and smart city. However, existing
centralized or federated learning paradigms have difficulties in coordinating
heterogeneous resources in distributed IoT environments. In this article, we
introduce a semi-federated learning (SemiFL) framework to tackle the challenges
of data and device heterogeneity in massive IoT networks. In SemiFL, only users
with sufficient computing resources are selected for local model training,
while the remaining users only transmit raw data to the base station for remote
computing. By doing so, SemiFL incorporates conventional centralized and
federated learning paradigms into a harmonized framework that allows all
devices to participate in the global model training regardless of their
computational capabilities and data distributions. Furthermore, we propose a
next-generation multiple access scheme by seamlessly integrating communication
and computation over the air. This achieves the concurrent transmission of raw
data and model parameters in a spectrum-efficient manner. With their abilities
to change channels and charge devices, two emerging techniques, reconfigurable
intelligent surface and wireless energy transfer, are merged with our SemiFL
framework to enhance its performance in bandwidth- and energy-limited IoT
networks, respectively. Simulation results are presented to demonstrate the
superiority of our SemiFL for achieving edge intelligence among
computing-heterogeneous IoT devices.
