  As the number of sensors becomes massive in Internet of Things (IoT)
networks, the amount of data is humongous. To process data in real-time while
protecting user privacy, federated learning (FL) has been regarded as an
enabling technique to push edge intelligence into IoT networks with massive
devices. However, FL latency increases dramatically due to the increase of the
number of parameters in deep neural network and the limited computation and
communication capabilities of IoT devices. To address this issue, we propose a
semi-federated learning (SemiFL) paradigm in which network pruning and
over-the-air computation are efficiently applied. To be specific, each small
base station collects the raw data from its served sensors and trains its local
pruned model. After that, the global aggregation of local gradients is achieved
through over-the-air computation. We first analyze the performance of the
proposed SemiFL by deriving its convergence upper bound. To reduce latency, a
convergence-constrained SemiFL latency minimization problem is formulated. By
decoupling the original problem into several sub-problems, iterative algorithms
are designed to solve them efficiently. Finally, numerical simulations are
conducted to verify the effectiveness of our proposed scheme in reducing
latency and guaranteeing the identification accuracy.
