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.

翻译：在大规模物联网网络中实施现有联邦学习面临着关键挑战，例如数据不平衡且统计异质性高、设备多样性强。为此，我们提出半联邦学习框架，为智能物联网的实现提供潜在解决方案。通过无缝融合集中式与联邦式范式，我们的半联邦学习框架在存在计算受限传感器的情况下，仍展现出对物联网设备数量的高扩展性。此外，与传统学习方法相比，由于边缘服务器与本地设备间的协同模型训练，所提半联邦学习能更充分利用分布式数据与计算资源。仿真结果验证了该半联邦学习框架在大规模物联网网络中的有效性。代码可通过 https://github.com/niwanli/SemiFL_IoT 获取。