# Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?

arXiv: https://arxiv.org/abs/2003.12365 · HTML: https://mlsanity.com/paper/2003.12365/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 30 of 50)

- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Practical Defences Against Model Inversion Attacks for Split Neural Networks](https://mlsanity.com/paper/2104.05743/) (2021)
- [Differentially Private Label Protection in Split Learning](https://mlsanity.com/paper/2203.02073/) (2022)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Vertical Federated Learning without Revealing Intersection Membership](https://mlsanity.com/paper/2106.05508/) (2021)
- [ExPLoit: Extracting Private Labels in Split Learning](https://mlsanity.com/paper/2112.01299/) (2021)
- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning](https://mlsanity.com/paper/2301.08778/) (2023)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [Communication and Computation Reduction for Split Learning using Asynchronous Training](https://mlsanity.com/paper/2107.09786/) (2021)
- [Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction](https://mlsanity.com/paper/2206.04864/) (2022)
- [HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2212.00325/) (2022)
- [Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2309.10517/) (2023)
- [Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures](https://mlsanity.com/paper/2403.14772/) (2024)
- [DPAUC: Differentially Private AUC Computation in Federated Learning](https://mlsanity.com/paper/2208.12294/) (2022)
- [GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2401.14840/) (2024)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [Secure Transformer Inference Protocol](https://mlsanity.com/paper/2312.00025/) (2023)
- [Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning](https://mlsanity.com/paper/2404.09265/) (2024)
- [Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems](https://mlsanity.com/paper/2503.00383/) (2025)
- [Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation](https://mlsanity.com/paper/2108.07463/) (2021)
- [LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data](https://mlsanity.com/paper/2203.04305/) (2022)
- [A Hybrid Quantum Neural Network for Split Learning](https://mlsanity.com/paper/2409.16593/) (2024)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Protecting Split Learning by Potential Energy Loss](https://mlsanity.com/paper/2210.09617/) (2022)
- [PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference](https://mlsanity.com/paper/2211.06716/) (2022)
- [SLPerf: a Unified Framework for Benchmarking Split Learning](https://mlsanity.com/paper/2304.01502/) (2023)
- [Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things](https://mlsanity.com/paper/2311.04944/) (2023)

## Abstract (arXiv)

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server. Therefore, the server has no direct access to raw data processed at the client. Until now, the split learning is believed to be a promising approach to protect the client's raw data; for example, the client's data was protected in healthcare image applications using 2D convolutional neural network (CNN) models. However, it is still unclear whether the split learning can be applied to other deep learning models, in particular, 1D CNN. In this paper, we examine whether split learning can be used to perform privacy-preserving training for 1D CNN models. To answer this, we first design and implement an 1D CNN model under split learning and validate its efficacy in detecting heart abnormalities using medical ECG data. We observed that the 1D CNN model under split learning can achieve the same accuracy of 98.9% like the original (non-split) model. However, our evaluation demonstrates that split learning may fail to protect the raw data privacy on 1D CNN models. To address the observed privacy leakage in split learning, we adopt two privacy leakage mitigation techniques: 1) adding more hidden layers to the client side and 2) applying differential privacy. Although those mitigation techniques are helpful in reducing privacy leakage, they have a significant impact on model accuracy. Hence, based on those results, we conclude that split learning alone would not be sufficient to maintain the confidentiality of raw sequential data in 1D CNN models.