mlsanityData to 7 Oct 2026

ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning

arXiv 2603.04437 ↗First posted 19 Feb 2026cs.LG cs.AIcited by 1

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Abstract (arXiv)

Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption on training. In this paper, we propose an adaptive split federated learning (ASFL) framework over wireless networks. ASFL exploits the computation resources of the central server to train part of the model and enables adaptive model splitting as well as resource allocation during training. To optimize the learning performance (i.e., convergence rate) and efficiency (i.e., delay and energy consumption) of ASFL, we theoretically analyze the convergence rate and formulate a joint learning performance and resource allocation optimization problem. Solving this problem is challenging due to the long-term delay and energy consumption constraints as well as the coupling of the model splitting and resource allocation decisions. We propose an online optimization enhanced block coordinate descent (OOE-BCD) algorithm to solve the problem iteratively. Experimental results show that when compared with five baseline schemes, our proposed ASFL framework converges faster and reduces the total delay and energy consumption by up to 75% and 80%, respectively.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 63 references that are in the map. First posted on arXiv 19 Feb 2026.

Builds on

36 of 63 references.

References · 36

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24 more
  1. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  2. 2018Distributed learning of deep neural network over multiple agentscited by 277
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
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  9. 2023Split Learning in 6G Edge Networkscited by 68
  10. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  11. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  12. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 34
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  14. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
  15. 2022SplitGP: Achieving Both Generalization and Personalization in Federated Learningcited by 20
  16. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  17. 2024Convergence Analysis of Split Federated Learning on Heterogeneous Datacited by 17
  18. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
  19. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  20. 2023A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospectscited by 8
  21. 2024Workflow Optimization for Parallel Split Learningcited by 8
  22. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  23. 2025Split Fine-Tuning for Large Language Models in Wireless Networkscited by 6
  24. 2023Convergence Analysis of Sequential Split Learning on Heterogeneous Datacited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Efficient Resource Optimization for Split Federated Learningcited by 0