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

• URL: http://arxiv.org/abs/2602.02696v1
• Date: Mon, 02 Feb 2026 19:08:34 GMT
• Title: NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning
• Authors: Zhen Fang, Miao Yang, Zehang Lin, Zheng Lin, Zihan Fang, Zongyuan Zhang, Tianyang Duan, Dong Huang, Shunzhi Zhu,
• Abstract summary: NSC-SL is a bandwidth-aware adaptive compression algorithm for communication-efficient split learning.<n> NSC-SL achieves high compression ratios while preserving semantic-rich information essential for convergence.
• Score: 22.576107330283744
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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