Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 8 of its 11 references that are in the map. First posted on arXiv 9 Mar 2023.
Builds on
8 of 11 references.
References · 8
- 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
- 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
- 2021Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?cited by 13
- 2021STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimizationcited by 8
- 2020Federated Learning in Multi-RIS Aided Systemscited by 7
- 2022Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Dronescited by 7
- 2020Hybrid Federated and Centralized Learningcited by 5
- 2022Towards Communication-Learning Trade-off for Federated Learning at the Network Edgecited by 5
Built on by
Citing papers per year: 2023: 2; 2024: 2; 2025: 2; 2026: 1.
7 papers.
- 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
- 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learningcited by 2
- 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Schedulingcited by 1
- 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Frameworkcited by 0
- 2024H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applicationscited by 0
- 2025Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Modelscited by 0
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learningcited by 0
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning11 Mar 2026
- 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling3 Jun 2025
- 2025Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models26 Mar 2025
- 2024H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications9 Dec 2024
- 2024How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning24 Jan 2024
- 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework4 Oct 2023
- 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates21 Jun 2023