# Split Learning for collaborative deep learning in healthcare

arXiv: https://arxiv.org/abs/1912.12115 · HTML: https://mlsanity.com/paper/1912.12115/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 30 of 73)

- [FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning](https://mlsanity.com/paper/2111.08211/) (2021)
- [Optimal Resource Allocation for U-Shaped Parallel Split Learning](https://mlsanity.com/paper/2308.08896/) (2023)
- [Knowledge Distillation for Federated Learning: a Practical Guide](https://mlsanity.com/paper/2211.04742/) (2022)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Federated Learning of Large Models at the Edge via Principal Sub-Model Training](https://mlsanity.com/paper/2208.13141/) (2022)
- [Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information](https://mlsanity.com/paper/2305.04146/) (2023)
- [ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection](https://mlsanity.com/paper/2312.14227/) (2023)
- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance](https://mlsanity.com/paper/2109.09246/) (2021)
- [Robust Split Federated Learning for U-shaped Medical Image Networks](https://mlsanity.com/paper/2212.06378/) (2022)
- [Passive Inference Attacks on Split Learning via Adversarial Regularization](https://mlsanity.com/paper/2310.10483/) (2023)
- [Personalized Federated Deep Learning for Pain Estimation From Face Images](https://mlsanity.com/paper/2101.04800/) (2021)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Federated Learning-based Active Authentication on Mobile Devices](https://mlsanity.com/paper/2104.07158/) (2021)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling](https://mlsanity.com/paper/2306.14638/) (2023)
- [A Mutual Information Perspective on Federated Contrastive Learning](https://mlsanity.com/paper/2405.02081/) (2024)
- [SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)](https://mlsanity.com/paper/2501.06650/) (2025)
- [Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach](https://mlsanity.com/paper/2510.21155/) (2025)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [An Efficient Privacy-aware Split Learning Framework for Satellite Communications](https://mlsanity.com/paper/2409.08538/) (2024)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning](https://mlsanity.com/paper/2206.03852/) (2022)
- [Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2208.10553/) (2022)
- [SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection](https://mlsanity.com/paper/2302.08618/) (2023)
- [Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations](https://mlsanity.com/paper/2304.14976/) (2023)
- [Split Unlearning](https://mlsanity.com/paper/2308.10422/) (2023)
- [Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information](https://mlsanity.com/paper/2209.10119/) (2022)
- [FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training](https://mlsanity.com/paper/2211.10948/) (2022)

## Abstract (arXiv)

Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up. Distributed machine learning methods promise to mitigate these problems. We argue for a split learning based approach and apply this distributed learning method for the first time in the medical field to compare performance against (1) centrally hosted and (2) non collaborative configurations for a range of participants. Two medical deep learning tasks are used to compare split learning to conventional single and multi center approaches: a binary classification problem of a data set of 9000 fundus photos, and multi-label classification problem of a data set of 156,535 chest X-rays. The several distributed learning setups are compared for a range of 1-50 distributed participants. Performance of the split learning configuration remained constant for any number of clients compared to a single center study, showing a marked difference compared to the non collaborative configuration after 2 clients (p < 0.001) for both sets. Our results affirm the benefits of collaborative training of deep neural networks in health care. Our work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations.