# C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning

arXiv: https://arxiv.org/abs/2207.12397 · HTML: https://mlsanity.com/paper/2207.12397/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://mlsanity.com/paper/1910.14315/) (2019)
- [BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1902.01000/) (2019)
- [End-to-end Learning of Compressible Features](https://mlsanity.com/paper/2007.11797/) (2020)
- [JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution](https://mlsanity.com/paper/1812.10027/) (2018)
- [Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms](https://mlsanity.com/paper/1802.03835/) (2018)
- [Supervised Compression for Resource-Constrained Edge Computing Systems](https://mlsanity.com/paper/2108.11898/) (2021)
- [Back-and-Forth prediction for deep tensor compression](https://mlsanity.com/paper/2002.07036/) (2020)

## Built on by (most cited first, 8 of 8)

- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks](https://mlsanity.com/paper/2411.06773/) (2024)
- [SLPerf: a Unified Framework for Benchmarking Split Learning](https://mlsanity.com/paper/2304.01502/) (2023)
- [Ampere: Communication-Efficient and High-Accuracy Split Federated Learning](https://mlsanity.com/paper/2507.07130/) (2025)
- [Communication Efficient Split Learning of ViTs with Attention-based Double Compression](https://mlsanity.com/paper/2509.15058/) (2025)
- [AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning](https://mlsanity.com/paper/2607.17913/) (2026)
- [LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections](https://mlsanity.com/paper/2605.13265/) (2026)
- [Contribution-Aware Bandwidth Allocation for Multimodal Split Learning](https://mlsanity.com/paper/2609.01406/) (2026)

## Abstract (arXiv)

Most existing studies improve the efficiency of Split learning (SL) by compressing the transmitted features. However, most works focus on dimension-wise compression that transforms high-dimensional features into a low-dimensional space. In this paper, we propose circular convolution-based batch-wise compression for SL (C3-SL) to compress multiple features into one single feature. To avoid information loss while merging multiple features, we exploit the quasi-orthogonality of features in high-dimensional space with circular convolution and superposition. To the best of our knowledge, we are the first to explore the potential of batch-wise compression under the SL scenario. Based on the simulation results on CIFAR-10 and CIFAR-100, our method achieves a 16x compression ratio with negligible accuracy drops compared with the vanilla SL. Moreover, C3-SL significantly reduces 1152x memory and 2.25x computation overhead compared to the state-of-the-art dimension-wise compression method.