mlsanityData to 14 Oct 2026

Split Learning in 6G Edge Networks

arXiv 2306.12194 ↗First posted 21 Jun 2023cs.LG cs.DC cs.NIcited by 68

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Abstract (arXiv)

With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorporate federated learning into the mobile edge has gained considerable interest in recent years. However, the deployment of federated learning faces substantial challenges as massive resource-limited IoT devices can hardly support on-device model training. This leads to the emergence of split learning (SL) which enables servers to handle the major training workload while still enhancing data privacy. In this article, we offer a brief overview of key advancements in SL and articulate its seamless integration with wireless edge networks. We begin by illustrating the tailored 6G architecture to support edge SL. Then, we examine the critical design issues for edge SL, including innovative resource-efficient learning frameworks and resource management strategies under a single edge server. Additionally, we expand the scope to multi-edge scenarios, exploring multi-edge collaboration and mobility management from a networking perspective. Finally, we discuss open problems for edge SL, including convergence analysis, asynchronous SL and U-shaped SL.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 21 Jun 2023.

Builds on

9 of 20 references.

References · 9

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  3. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  4. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  5. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  6. 2020Communication-Computation Trade-Off in Resource-Constrained Edge Inferencecited by 60
  7. 2022A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networkscited by 22
  8. 2021Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performancecited by 11
  9. 2022C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learningcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 25; 2025: 26; 2026: 14.

68 papers.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
  2. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 90
  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 62
  5. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  6. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  7. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  8. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  9. 2025HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 31
  10. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
  11. 2024AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Mergingcited by 22
  12. 2025LEO-Split: A Semi-Supervised Split Learning Framework over LEO Satellite Networkscited by 22
38 more
  1. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  2. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  3. 2025Secure On-Device Video OOD Detection Without Backpropagationcited by 19
  4. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  5. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14
  6. 2024CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perceptioncited by 13
  7. 2024TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networkscited by 12
  8. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  9. 2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
  10. 2024A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentialitycited by 8
  11. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  12. 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
  13. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  14. 2025Rethinking Membership Inference Attacks Against Transfer Learningcited by 6
  15. 2025Revisiting Outage for Edge Inference Systemscited by 5
  16. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
  17. 2025PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inferencecited by 4
  18. 2025Satellite Edge Artificial Intelligence with Large Models: Architectures and Technologiescited by 4
  19. 2026NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learningcited by 4
  20. 2024Privacy-preserving Universal Adversarial Defense for Black-box Modelscited by 3
  21. 2025Visual Fidelity Index for Generative Semantic Communications with Critical Information Embeddingcited by 3
  22. 2025Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generationcited by 3
  23. 2026Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocationcited by 3
  24. 2024S2NeRF: Privacy-preserving Training Framework for NeRFcited by 2
  25. 2024Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformationcited by 2
  26. 2025Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edgecited by 2
  27. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 2
  28. 2025P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devicescited by 2
  29. 2025Communication Efficient Split Learning of ViTs with Attention-based Double Compressioncited by 2
  30. 2024t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Drivingcited by 1
  31. 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagationcited by 1
  32. 2025Secure Resource Allocation via Constrained Deep Reinforcement Learningcited by 1
  33. 2025Efficient Split Federated Learning for Large Language Models over Communication Networkscited by 1
  34. 2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1
  35. 2025Generative Feature Imputing -- A Technique for Error-resilient Semantic Communicationcited by 1
  36. 2025CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networkscited by 1
  37. 2025Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systemscited by 1
  38. 2026NOIR: Privacy-Preserving Generation of Code with Open-Source LLMscited by 1

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