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Split Learning over Wireless Networks: Parallel Design and Resource Management

arXiv 2204.08119 ↗First posted 18 Apr 2022cs.NIcited by 74

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Abstract (arXiv)

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, named Cluster-based Parallel SL (CPSL) which conducts model training in a "first-parallel-then-sequential" manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each cluster and aggregate them, and then sequentially train the whole AI model across clusters, thereby parallelizing the training process and reducing training latency. Furthermore, we propose a resource management algorithm to minimize the training latency of CPSL considering device heterogeneity and network dynamics in wireless networks. This is achieved by stochastically optimizing the cut layer selection, real-time device clustering, and radio spectrum allocation. The proposed two-timescale algorithm can jointly make the cut layer selection decision in a large timescale and device clustering and radio spectrum allocation decisions in a small timescale. Extensive simulation results on non-independent and identically distributed data demonstrate that the proposed solutions can greatly reduce the training latency as compared with the existing SL benchmarks, while adapting to network dynamics.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 15; 2024: 28; 2025: 16; 2026: 14.

74 papers.

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  1. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  2. 2023Split Learning in 6G Edge Networkscited by 68
  3. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  4. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  5. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  6. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 30
  7. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  8. 2024WDMoE: Wireless Distributed Large Language Models with Mixture of Expertscited by 18
  9. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
  10. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  11. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  12. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
38 more
  1. 2023NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Servicescited by 13
  2. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  3. 2023Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learningcited by 9
  4. 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devicescited by 9
  5. 2024Workflow Optimization for Parallel Split Learningcited by 8
  6. 2025FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimationcited by 7
  7. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
  8. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  9. 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
  10. 2024Adaptive Split Learning over Energy-Constrained Wireless Edge Networkscited by 4
  11. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  12. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
  13. 2024Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimizationcited by 4
  14. 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3
  15. 2023Training Latency Minimization for Model-Splitting Allowed Federated Edge Learningcited by 3
  16. 2023Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Thingscited by 3
  17. 2023Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approachcited by 3
  18. 2024MP-SL: Multihop Parallel Split Learningcited by 3
  19. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  20. 2024Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approachcited by 3
  21. 2024Parallel Split Learning with Global Samplingcited by 3
  22. 2024Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networkscited by 3
  23. 2025Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learningcited by 3
  24. 2024Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformercited by 2
  25. 2024CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authenticationcited by 2
  26. 2024Split Learning in Computer Vision for Semantic Segmentation Delay Minimizationcited by 2
  27. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  28. 2025Collaborative Split Federated Learning with Parallel Training and Aggregationcited by 2
  29. 2026Lean Clients, Full Accuracy: Hybrid Zeroth- and First-Order Split Federated Learningcited by 2
  30. 2023Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicingcited by 1
  31. 2023Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theorycited by 1
  32. 2024Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Trainingcited by 1
  33. 2024Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classificationcited by 1
  34. 2024Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimationcited by 1
  35. 2024Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignmentcited by 1
  36. 2025A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networkscited by 1
  37. 2025Efficient Split Federated Learning for Large Language Models over Communication Networkscited by 1
  38. 2025Fast AI Model Partition for Split Learning over Edge Networkscited by 1

50 most cited of 74 All 74 (JSON)