Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
This addresses data and computing limitations for IoT networks, but appears incremental as it builds on existing federated learning approaches.
The paper tackles the challenges of imbalanced data and device diversity in massive IoT networks by proposing a semi-federated learning framework that integrates centralized and federated paradigms, showing high scalability and better use of distributed resources.
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.