mlsanityData to 7 Oct 2026

Optimal Resource Allocation for U-Shaped Parallel Split Learning

arXiv 2308.08896 ↗First posted 17 Aug 2023cs.LGcited by 33

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Abstract (arXiv)

Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks label privacy as the tail model (with the last layers) should be placed on the server. To overcome this limitation, one promising solution is to utilize U-shaped architecture to leave both early layers and last layers on the user side. In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks. In the proposed framework, multiple users communicate with an edge server for SL. We analyze the end-to-end delay of each client during the training process and design an efficient resource allocation algorithm, called LSCRA, which finds the optimal computing resource allocation and split layers. Our experimental results show the effectiveness of LSCRA and that U-shaped parallel split learning can achieve a similar performance with other SL baselines while preserving label privacy. Index Terms: U-shaped network, split learning, label privacy, resource allocation, 5G/6G edge networks.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 17 Aug 2023.

Builds on

14 of 31 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  4. 2020SplitFed: When Federated Learning Meets Split Learningcited by 308
  5. 2018Distributed learning of deep neural network over multiple agentscited by 277
  6. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  7. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  8. 2019Split Learning for collaborative deep learning in healthcarecited by 72
  9. 2023Split Learning in 6G Edge Networkscited by 68
  10. 2017Data-driven Advice for Applying Machine Learning to Bioinformatics Problemscited by 16
  11. 2023Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Citiescited by 15
  12. 2022Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learningcited by 11
2 more
  1. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  2. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 13; 2025: 11; 2026: 8.

33 papers.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  3. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  4. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  5. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  6. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  7. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  8. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  9. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  10. 2025HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systemscited by 15
  11. 2024TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networkscited by 12
  12. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
21 more
  1. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  2. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  3. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 5
  4. 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
  5. 2026NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learningcited by 4
  6. 2025Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generationcited by 3
  7. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  8. 2025Sample Efficient Experience Replay in Non-stationary Environmentscited by 2
  9. 2024t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Drivingcited by 1
  10. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 1
  11. 2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1
  12. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  13. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  14. 2026The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Modelscited by 1
  15. 2024Tracing Human Stress from Physiological Signals using UWB Radarcited by 0
  16. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  17. 2025ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)cited by 0
  18. 2026SIDeR: Semantic Identity Decoupling for Unrestricted Face Privacycited by 0
  19. 2026Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Drivingcited by 0
  20. 2026USPLIT-VQA: U-Shaped Split Learning for Visual Question Answering with Contribution-Aware Weighted Aggregationcited by 0
  21. 2026Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimizationcited by 0