# Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning

arXiv: https://arxiv.org/abs/1904.06049 · HTML: https://mlsanity.com/paper/1904.06049/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction](https://mlsanity.com/paper/2206.04864/) (2022)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)

## Abstract (arXiv)

Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate training. To adequately protect sensitive information, we propose distributed layer-partitioned training with step-wise activation functions for privacy-preserving deep learning. Experimental results attest our method to be simple and effective.