mlsanityData to 14 Oct 2026

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

arXiv 2311.13163 ↗First posted 22 Nov 2023cs.LG cs.DCcited by 6

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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.

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

28 of 47 references.

References · 28

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16 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2025Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approachcited by 7
  2. 2024GAS: Generative Activation-Aided Asynchronous Split Federated Learningcited by 3
  3. 2024CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balancecited by 2
  4. 2025Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Modelscited by 1
  5. 2024FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutationcited by 0
  6. 2026A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizationscited by 0