mlsanityData to 14 Oct 2026

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning

arXiv 2504.09114 ↗First posted 12 Apr 2025cs.LGcited by 1

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Abstract (arXiv)

Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations across various industries. Yet, their practical deployment on resource-limited mobile edge devices is hindered by critical challenges such as data privacy, constrained resources, and high overhead costs. Addressing this gap, this paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM). By partitioning the training load between edge devices and servers using a split learning paradigm, SFLAM can facilitate the operation of large models on devices and significantly lowers the memory requirements on edge devices. Additionally, SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency while concurrently reducing energy consumption and communication latency. A theoretical analysis exploring the latency-energy trade-off is presented, and the framework's efficacy is validated via comprehensive simulations. The findings indicate that SFLAM achieves superior performance in terms of learning efficiency and scalability compared to conventional methods, thereby providing a valuable approach for enabling advanced AI services in resource-constrained scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 12 Apr 2025.

Builds on

26 of 44 references.

References · 26

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14 more
  1. 2022A Unified Analysis of Federated Learning with Arbitrary Client Participationcited by 45
  2. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  3. 2023Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directionscited by 32
  4. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  5. 2024Convergence Analysis of Split Federated Learning on Heterogeneous Datacited by 17
  6. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  7. 2024Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inferencecited by 12
  8. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  9. 2023FeSViBS: Federated Split Learning of Vision Transformer with Block Samplingcited by 7
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  11. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  12. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
  13. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  14. 2024Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design and Future Directionscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edgecited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2026TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks
  4. 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning
  5. 2024On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks
  6. 2026Separating Intelligence from Inference: A Standard for Edge-Native AI Computing ↗
  7. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  8. 2025Energy and Memory-Efficient Federated Learning With Ordered Layer Freezing