# Semi-Federated Learning

arXiv: https://arxiv.org/abs/2003.12795 · HTML: https://mlsanity.com/paper/2003.12795/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion](https://mlsanity.com/paper/2306.14088/) (2023)
- [DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology](https://mlsanity.com/paper/2111.05505/) (2021)
- [Dataset Distillation-based Hybrid Federated Learning on Non-IID Data](https://mlsanity.com/paper/2409.17517/) (2024)
- [RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence](https://mlsanity.com/paper/2310.07268/) (2023)

## Abstract (arXiv)

Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less up-link transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.