# SplitNN-driven Vertical Partitioning

arXiv: https://arxiv.org/abs/2008.04137 · HTML: https://mlsanity.com/paper/2008.04137/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 30 of 42)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN](https://mlsanity.com/paper/2104.00489/) (2021)
- [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)
- [Vertical Federated Learning without Revealing Intersection Membership](https://mlsanity.com/paper/2106.05508/) (2021)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [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)
- [LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2305.02219/) (2023)
- [Vertical Semi-Federated Learning for Efficient Online Advertising](https://mlsanity.com/paper/2209.15635/) (2022)
- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication](https://mlsanity.com/paper/2201.08512/) (2022)
- [A Split-and-Privatize Framework for Large Language Model Fine-Tuning](https://mlsanity.com/paper/2312.15603/) (2023)
- [Passive Inference Attacks on Split Learning via Adversarial Regularization](https://mlsanity.com/paper/2310.10483/) (2023)
- [In-Network Learning: Distributed Training and Inference in Networks](https://mlsanity.com/paper/2107.03433/) (2021)
- [Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks](https://mlsanity.com/paper/2304.09515/) (2023)
- [DPAUC: Differentially Private AUC Computation in Federated Learning](https://mlsanity.com/paper/2208.12294/) (2022)
- [Vertical Federated Learning with Missing Features During Training and Inference](https://mlsanity.com/paper/2410.22564/) (2024)
- [Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners](https://mlsanity.com/paper/2106.05468/) (2021)
- [VFed-SSD: Towards Practical Vertical Federated Advertising](https://mlsanity.com/paper/2205.15987/) (2022)
- [A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning](https://mlsanity.com/paper/2309.09977/) (2023)
- [Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection](https://mlsanity.com/paper/2401.10765/) (2024)
- [Communication-efficient Vertical Federated Learning via Compressed Error Feedback](https://mlsanity.com/paper/2406.14420/) (2024)
- [Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario](https://mlsanity.com/paper/2210.11061/) (2022)
- [Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data](https://mlsanity.com/paper/2301.11468/) (2023)
- [Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments](https://mlsanity.com/paper/2312.16638/) (2023)
- [Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference](https://mlsanity.com/paper/2405.04043/) (2024)
- [Towards Active Participant Centric Vertical Federated Learning: Some Representations May Be All You Need](https://mlsanity.com/paper/2410.17648/) (2024)
- [Floe: Federated Specialization for Real-Time LLM-SLM Inference](https://mlsanity.com/paper/2602.14302/) (2026)

## Abstract (arXiv)

In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features. SplitNN does not share raw data or model details with collaborating institutions. The proposed configuration allows training among institutions holding diverse sources of data without the need of complex encryption algorithms or secure computation protocols. We evaluate several configurations to merge the outputs of the split models, and compare performance and resource efficiency. The method is flexible and allows many different configurations to tackle the specific challenges posed by vertically split datasets.