# Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization

arXiv: https://arxiv.org/abs/2411.13907 · HTML: https://mlsanity.com/paper/2411.13907/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)

## Built on by (most cited first, 4 of 4)

- [Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learning](https://mlsanity.com/paper/2507.07323/) (2025)
- [P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices](https://mlsanity.com/paper/2507.17228/) (2025)
- [A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraints](https://mlsanity.com/paper/2507.12979/) (2025)
- [StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting](https://mlsanity.com/paper/2605.26523/) (2026)

## Abstract (arXiv)

Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However, current SL algorithms face limitations in training efficiency and suffer from prolonged latency, particularly in sequential settings, where the slowest device can bottleneck the entire process due to heterogeneous resources and frequent data exchanges between clients and servers. To address these challenges, we propose the Heterogeneous Split Federated Learning (HSFL) framework, which allows resource-constrained clients to train their personalized client-side models in parallel, utilizing different cut layers. Aiming to mitigate the impact of heterogeneous environments and accelerate the training process, we formulate a latency minimization problem that optimizes computational and transmission resources jointly. Additionally, we design a resource allocation algorithm that combines the Sample Average Approximation (SAA), Genetic Algorithm (GA), Lagrangian relaxation and Branch and Bound (B&B) methods to efficiently solve this problem. Simulation results demonstrate that HSFL outperforms other frameworks in terms of both convergence rate and model accuracy on heterogeneous devices with non-iid data, while the optimization algorithm is better than other baseline methods in reducing latency.