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.

翻译：随着物联网网络中传感器数量变得庞大，数据量也极其巨大。为了在保护用户隐私的同时实时处理数据，联邦学习被视为一种将边缘智能引入大规模设备物联网网络的关键技术。然而，由于深度神经网络参数数量的增加以及物联网设备计算与通信能力有限，联邦学习的时延显著增长。为解决这一问题，我们提出了一种半联邦学习范式，其中有效应用了网络剪枝和空中计算技术。具体而言，每个小型基站从其服务的传感器收集原始数据，并训练其局部剪枝模型。之后，通过空中计算实现局部梯度的全局聚合。我们首先通过推导所提半联邦学习的收敛上界来分析其性能。为降低时延，构建了一个受收敛约束的半联邦学习时延最小化问题。通过将原始问题分解为若干子问题，设计了迭代算法以高效求解。最后，通过数值仿真验证了所提方案在降低时延和保证识别准确率方面的有效性。