# Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks

arXiv: https://arxiv.org/abs/2303.05048 · HTML: https://mlsanity.com/paper/2303.05048/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge](https://mlsanity.com/paper/2001.10402/) (2020)
- [Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2004.05843/) (2020)
- [Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?](https://mlsanity.com/paper/2103.00435/) (2021)
- [STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization](https://mlsanity.com/paper/2106.08592/) (2021)
- [Federated Learning in Multi-RIS Aided Systems](https://mlsanity.com/paper/2010.13333/) (2020)
- [Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones](https://mlsanity.com/paper/2201.01230/) (2022)
- [Hybrid Federated and Centralized Learning](https://mlsanity.com/paper/2011.06892/) (2020)
- [Towards Communication-Learning Trade-off for Federated Learning at the Network Edge](https://mlsanity.com/paper/2205.14271/) (2022)

## Built on by (most cited first, 7 of 7)

- [MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates](https://mlsanity.com/paper/2306.12212/) (2023)
- [How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning](https://mlsanity.com/paper/2401.13236/) (2024)
- [Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling](https://mlsanity.com/paper/2506.02422/) (2025)
- [Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework](https://mlsanity.com/paper/2310.02559/) (2023)
- [H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications](https://mlsanity.com/paper/2412.06210/) (2024)
- [Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models](https://mlsanity.com/paper/2503.20138/) (2025)
- [Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning](https://mlsanity.com/paper/2603.10763/) (2026)

## 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.