# ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues

arXiv: https://arxiv.org/abs/2410.01256 · HTML: https://mlsanity.com/paper/2410.01256/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout](https://mlsanity.com/paper/2503.10217/) (2025)
- [Ampere: Communication-Efficient and High-Accuracy Split Federated Learning](https://mlsanity.com/paper/2507.07130/) (2025)
- [Towards Robust Personalized Federated Learning: Vulnerability Assessment and Defense Co-Design](https://mlsanity.com/paper/2606.22782/) (2026)
- [Personalized Hierarchical Split Federated Learning in Wireless Networks](https://mlsanity.com/paper/2411.06042/) (2024)
- [Collaborative Inference for Large Models with Task Offloading and Early Exiting](https://mlsanity.com/paper/2412.08284/) (2024)
- [Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions](https://mlsanity.com/paper/2510.05713/) (2025)
- [Robust Client-Server Watermarking for Split Federated Learning](https://mlsanity.com/paper/2511.13598/) (2025)
- [BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregation](https://mlsanity.com/paper/2605.17508/) (2026)
- [Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning](https://mlsanity.com/paper/2607.08137/) (2026)
- [When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation](https://mlsanity.com/paper/2608.15639/) (2026)

## Abstract (arXiv)

Mobile devices contribute more than half of the world's web traffic, providing massive and diverse data for powering various federated learning (FL) applications. In order to avoid the communication bottleneck on the parameter server (PS) and accelerate the training of large-scale models on resourceconstraint workers in edge computing (EC) system, we propose a novel split federated learning (SFL) framework, termed ParallelSFL. Concretely, we split an entire model into a bottom submodel and a top submodel, and divide participating workers into multiple clusters, each of which collaboratively performs the SFL training procedure and exchanges entire models with the PS. However, considering the statistical and system heterogeneity in edge systems, it is challenging to arrange suitable workers to specific clusters for efficient model training. To address these challenges, we carefully develop an effective clustering strategy by optimizing a utility function related to training efficiency and model accuracy. Specifically, ParallelSFL partitions workers into different clusters under the heterogeneity restrictions, thereby promoting model accuracy as well as training efficiency. Meanwhile, ParallelSFL assigns diverse and appropriate local updating frequencies for each cluster to further address system heterogeneity. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson devices, and the experimental results show that ParallelSFL can reduce the traffic consumption by at least 21%, speed up the model training by at least 1.36x, and improve model accuracy by at least 5% in heterogeneous scenarios, compared to the baselines.