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Exploiting Adaptive Channel Pruning for Communication-Efficient Split Learning

Jialei Tan, Zheng Lin, Xiangming Cai, Ruoxi Zhu, Zihan Fang, Pingping Chen, Wei Ni · Mar 10, 2026 · Citations: 0

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How to use this page

Provisional trust

This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Provisional

Derived from abstract and metadata only.

Abstract

Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices. However, the transmission of intermediate feature representations, referred to as smashed data, incurs significant communication overhead, particularly when a large number of client devices are involved. To address this challenge, we propose an adaptive channel pruning-aided SL (ACP-SL) scheme. In ACP-SL, a label-aware channel importance scoring (LCIS) module is designed to generate channel importance scores, distinguishing important channels from less important ones. Based on these scores, an adaptive channel pruning (ACP) module is developed to prune less important channels, thereby compressing the corresponding smashed data and reducing the communication overhead. Experimental results show that ACP-SL consistently outperforms benchmark schemes in test accuracy. Furthermore, it reaches a target test accuracy in fewer training rounds, thereby reducing communication overhead.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This page is still relying on abstract and metadata signals, not a fuller protocol read.
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Should You Rely On This Paper?

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

This page is still relying on abstract and metadata signals, not a fuller protocol read.

Trust level

Provisional

Usefulness score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Provisional (processing)

Extraction confidence 0%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

"Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices."

Evaluation Modes

provisional (inferred)

Automatic metrics

Includes extracted eval setup.

"Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices."

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

"Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices."

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

"Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices."

Reported Metrics

provisional (inferred)

Accuracy

Useful for evaluation criteria comparison.

"Experimental results show that ACP-SL consistently outperforms benchmark schemes in test accuracy."

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

"Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices."

Human Feedback Details

This page is using abstract-level cues only right now. Treat the signals below as provisional.

  • Potential human-data signal: No explicit human-data keywords detected.
  • Potential benchmark anchors: No benchmark names detected in abstract.
  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

Evaluation fields are inferred from the abstract only.

  • Potential evaluation modes: Automatic metrics
  • Potential metric signals: Accuracy
  • Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Split learning (SL) transfers most of the training workload to the server, which alleviates computational burden on client devices.
  • However, the transmission of intermediate feature representations, referred to as smashed data, incurs significant communication overhead, particularly when a large number of client devices are involved.
  • To address this challenge, we propose an adaptive channel pruning-aided SL (ACP-SL) scheme.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Accuracy

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