# The Impact of Cut Layer Selection in Split Federated Learning

arXiv: https://arxiv.org/abs/2412.15536 · HTML: https://mlsanity.com/paper/2412.15536/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [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)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Minibatch vs Local SGD for Heterogeneous Distributed Learning](https://mlsanity.com/paper/2006.04735/) (2020)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning](https://mlsanity.com/paper/2205.04007/) (2022)
- [Federated Edge Learning : Design Issues and Challenges](https://mlsanity.com/paper/2009.00081/) (2020)
- [Convergence Analysis of Sequential Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2311.03154/) (2023)
- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging](https://mlsanity.com/paper/2107.02375/) (2021)
- [Convergence Analysis of Split Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2402.15166/) (2024)
- [ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices](https://mlsanity.com/paper/2402.15903/) (2024)
- [MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation](https://mlsanity.com/paper/2311.13348/) (2023)
- [Robust Split Federated Learning for U-shaped Medical Image Networks](https://mlsanity.com/paper/2212.06378/) (2022)
- [Communication and Storage Efficient Federated Split Learning](https://mlsanity.com/paper/2302.05599/) (2023)
- [Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning](https://mlsanity.com/paper/2311.09441/) (2023)
- [FedUV: Uniformity and Variance for Heterogeneous Federated Learning](https://mlsanity.com/paper/2402.18372/) (2024)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients](https://mlsanity.com/paper/2310.14579/) (2023)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention](https://mlsanity.com/paper/2301.12885/) (2023)
- [SplitFed resilience to packet loss: Where to split, that is the question](https://mlsanity.com/paper/2307.13851/) (2023)

## Built on by (most cited first, 6 of 6)

- [Fast AI Model Partition for Split Learning over Edge Networks](https://mlsanity.com/paper/2507.01041/) (2025)
- [ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning](https://mlsanity.com/paper/2603.04437/) (2026)
- [Decoupled Split Learning via Auxiliary Loss](https://mlsanity.com/paper/2601.19261/) (2026)
- [QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning](https://mlsanity.com/paper/2606.09869/) (2026)
- [BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage](https://mlsanity.com/paper/2607.24556/) (2026)
- [Unveiling the Depth-Performance Dilemma in Split-Federated Fine-tuning of LLMs](https://mlsanity.com/paper/2608.22188/) (2026)

## Abstract (arXiv)

Split Federated Learning (SFL) is a distributed machine learning paradigm that combines federated learning and split learning. In SFL, a neural network is partitioned at a cut layer, with the initial layers deployed on clients and remaining layers on a training server. There are two main variants of SFL: SFL-V1 where the training server maintains separate server-side models for each client, and SFL-V2 where the training server maintains a single shared model for all clients. While existing studies have focused on algorithm development for SFL, a comprehensive quantitative analysis of how the cut layer selection affects model performance remains unexplored. This paper addresses this gap by providing numerical and theoretical analysis of SFL performance and convergence relative to cut layer selection. We find that SFL-V1 is relatively invariant to the choice of cut layer, which is consistent with our theoretical results. Numerical experiments on four datasets and two neural networks show that the cut layer selection significantly affects the performance of SFL-V2. Moreover, SFL-V2 with an appropriate cut layer selection outperforms FedAvg on heterogeneous data.