mlsanityData to 7 Oct 2026

MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation

arXiv 2311.13348 ↗First posted 22 Nov 2023cs.LG cs.DC cs.NIcited by 13

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Abstract (arXiv)

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication burden on resource-constrained workers and protect model privacy, split federated learning (SFL) has been released by integrating both data and model parallelism. Despite resource limitations, SFL still faces two other critical challenges in EC, i.e., statistical heterogeneity and system heterogeneity. To address these challenges, we propose a novel SFL framework, termed MergeSFL, by incorporating feature merging and batch size regulation in SFL. Concretely, feature merging aims to merge the features from workers into a mixed feature sequence, which is approximately equivalent to the features derived from IID data and is employed to promote model accuracy. While batch size regulation aims to assign diverse and suitable batch sizes for heterogeneous workers to improve training efficiency. Moreover, MergeSFL explores to jointly optimize these two strategies upon their coupled relationship to better enhance the performance of SFL. Extensive experiments are conducted on a physical platform with 80 NVIDIA Jetson edge devices, and the experimental results show that MergeSFL can improve the final model accuracy by 5.82% to 26.22%, with a speedup by about 1.74x to 4.14x, compared to the baselines.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 34 of its 83 references that are in the map. First posted on arXiv 22 Nov 2023.

Builds on

34 of 83 references.

References · 34

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22 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 3.

13 papers.

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  1. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  2. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  3. 2024Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devicescited by 5
  4. 2024Fair Concurrent Training of Multiple Models in Federated Learningcited by 3
  5. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  6. 2025Data Heterogeneity and Forgotten Labels in Split Federated Learningcited by 2
  7. 2025Traversal Learning: A Lossless And Efficient Distributed Learning Frameworkcited by 0
  8. 2025Collaborative Batch Size Optimization for Federated Learningcited by 0
  9. 2025SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Datacited by 0
  10. 2025Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimizationcited by 0
  11. 2026Makespan Minimization in Split Learning: From Theory to Practicecited by 0
  12. 2026BESplit: Bias-Compensated Split Federated Learning with Evidential Aggregationcited by 0
1 more
  1. 2026When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimationcited by 0