mlsanityData to 7 Oct 2026

NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning

arXiv 2602.02696 ↗First posted 2 Feb 2026cs.NI cs.LGcited by 4

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Abstract (arXiv)

The expanding scale of neural networks poses a major challenge for distributed machine learning, particularly under limited communication resources. While split learning (SL) alleviates client computational burden by distributing model layers between clients and server, it incurs substantial communication overhead from frequent transmission of intermediate activations and gradients. To tackle this issue, we propose NSC-SL, a bandwidth-aware adaptive compression algorithm for communication-efficient SL. NSC-SL first dynamically determines the optimal rank of low-rank approximation based on the singular value distribution for adapting real-time bandwidth constraints. Then, NSC-SL performs error-compensated tensor factorization using alternating orthogonal iteration with residual feedback, effectively minimizing truncation loss. The collaborative mechanisms enable NSC-SL to achieve high compression ratios while preserving semantic-rich information essential for convergence. Extensive experiments demonstrate the superb performance of NSC-SL.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

24 of 33 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  5. 2019PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimizationcited by 234
  6. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
  7. 2023Split Learning in 6G Edge Networkscited by 68
  8. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  9. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  10. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  11. 2023Matrix Compression via Randomized Low Rank and Low Precision Factorizationcited by 25
  12. 2024FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Datacited by 25
12 more
  1. 2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
  2. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  3. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  4. 2025Constructing 4D Radio Map in LEO Satellite Networks with Limited Samplescited by 13
  5. 2024Sums: Sniffing Unknown Multiband Signals under Low Sampling Ratescited by 12
  6. 2023Communication and Storage Efficient Federated Split Learningcited by 11
  7. 2025LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systemscited by 8
  8. 2023Evaluation and Optimization of Gradient Compression for Distributed Deep Learningcited by 7
  9. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
  10. 2025SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compressioncited by 6
  11. 2023Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informaticscited by 5
  12. 2025Pipelining Split Learning in Multi-hop Edge Networkscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compressioncited by 1
  2. 2026Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classificationcited by 1
  3. 2026SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoningcited by 0
  4. 2026Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimizationcited by 0