# Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning

arXiv: https://arxiv.org/abs/2311.13163 · HTML: https://mlsanity.com/paper/2311.13163/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 47 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (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)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction](https://mlsanity.com/paper/2203.11751/) (2022)
- [Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data](https://mlsanity.com/paper/2008.06180/) (2020)
- [Multi-Center Federated Learning: Clients Clustering for Better Personalization](https://mlsanity.com/paper/2108.08647/) (2021)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://mlsanity.com/paper/1910.01355/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers](https://mlsanity.com/paper/2010.05958/) (2020)
- [FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation](https://mlsanity.com/paper/2210.08285/) (2022)
- [A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee](https://mlsanity.com/paper/2108.00365/) (2021)
- [GitFL: Adaptive Asynchronous Federated Learning using Version Control](https://mlsanity.com/paper/2211.12049/) (2022)
- [HADFL: Heterogeneity-aware Decentralized Federated Learning Framework](https://mlsanity.com/paper/2111.08274/) (2021)

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

- [Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach](https://mlsanity.com/paper/2510.21155/) (2025)
- [GAS: Generative Activation-Aided Asynchronous Split Federated Learning](https://mlsanity.com/paper/2409.01251/) (2024)
- [CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance](https://mlsanity.com/paper/2404.12850/) (2024)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation](https://mlsanity.com/paper/2411.15847/) (2024)
- [A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations](https://mlsanity.com/paper/2604.24468/) (2026)

## Abstract (arXiv)

Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency. To address these issues, this paper presents a novel SFL approach, named Sliding Split Federated Learning (S2FL), which adopts an adaptive sliding model split strategy and a data balance-based training mechanism. By dynamically dispatching different model portions to AIoT devices according to their computing capability, S2FL can alleviate the low training efficiency caused by stragglers. By combining features uploaded by devices with different data distributions to generate multiple larger batches with a uniform distribution for back-propagation, S2FL can alleviate the performance degradation caused by data heterogeneity. Experimental results demonstrate that, compared to conventional SFL, S2FL can achieve up to 16.5% inference accuracy improvement and 3.54X training acceleration.