Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks

Abstract

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.

arXiv commentThe paper has been accepted for publication in the IEEE Internet of Things Journal

References in corpus (3)

• STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization
• Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?
• Towards Communication-Learning Trade-off for Federated Learning at the Network Edge

Cited by in corpus (5)

• MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework
• Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
• Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks
• Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling