mlsanityData to 14 Oct 2026

Accelerating Split Federated Learning over Wireless Communication Networks

arXiv 2310.15584 ↗First posted 24 Oct 2023cs.LG cs.NI eess.SPcited by 16

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Abstract (arXiv)

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

22 of 38 references.

References · 22

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  5. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  6. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  7. 2018Distributed learning of deep neural network over multiple agentscited by 279
  8. 2018Deep Face Recognition: A Surveycited by 276
  9. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  10. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  11. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  12. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
10 more
  1. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 152
  2. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  3. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  4. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  5. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  6. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  7. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  8. 2021Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inferencecited by 19
  9. 2022Accelerating Federated Edge Learning via Topology Optimizationcited by 4
  10. 2022Client Selection for Federated Bayesian Learningcited by 4

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Cited by year (2026 to 14 Oct)

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

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  1. 2024Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networkscited by 7
  2. 2024Adaptive Split Learning over Energy-Constrained Wireless Edge Networkscited by 4
  3. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 3
  4. 2025Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learningcited by 3
  5. 2024Optimizing Split Points for Error-Resilient SplitFed Learningcited by 1
  6. 2024Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classificationcited by 1
  7. 2025Efficient Split Federated Learning for Large Language Models over Communication Networkscited by 1
  8. 2025Fast AI Model Partition for Split Learning over Edge Networkscited by 1
  9. 2026ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learningcited by 1
  10. 2024Personalized Hierarchical Split Federated Learning in Wireless Networkscited by 0
  11. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  12. 2025Communication and Computation Efficient Split Federated Learning in O-RANcited by 0
4 more
  1. 2025Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directionscited by 0
  2. 2026Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Networkcited by 0
  3. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0
  4. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0