# Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

arXiv: https://arxiv.org/abs/2309.09086 · HTML: https://mlsanity.com/paper/2309.09086/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2023.

## Built on by (most cited first, 8 of 8)

- [Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning](https://mlsanity.com/paper/2311.09441/) (2023)
- [Split Learning in Computer Vision for Semantic Segmentation Delay Minimization](https://mlsanity.com/paper/2412.14272/) (2024)
- [Data Heterogeneity and Forgotten Labels in Split Federated Learning](https://mlsanity.com/paper/2511.09736/) (2025)
- [How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?](https://mlsanity.com/paper/2412.07813/) (2024)
- [Optimizing Split Learning Latency in TinyML-Based IoT Systems](https://mlsanity.com/paper/2507.16594/) (2025)
- [Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks](https://mlsanity.com/paper/2512.18432/) (2025)
- [Makespan Minimization in Split Learning: From Theory to Practice](https://mlsanity.com/paper/2602.06693/) (2026)
- [Contribution-Aware Bandwidth Allocation for Multimodal Split Learning](https://mlsanity.com/paper/2609.01406/) (2026)

## Abstract (arXiv)

Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.