# Spatio-Temporal Split Learning

arXiv: https://arxiv.org/abs/2108.06309 · HTML: https://mlsanity.com/paper/2108.06309/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms](https://mlsanity.com/paper/2001.02932/) (2020)

## Built on by (most cited first, 7 of 7)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data](https://mlsanity.com/paper/2108.10147/) (2021)
- [Quantum Split Neural Network Learning using Cross-Channel Pooling](https://mlsanity.com/paper/2211.06524/) (2022)
- [FedEmbed: Personalized Private Federated Learning](https://mlsanity.com/paper/2202.09472/) (2022)
- [Towards Balancing Preference and Performance through Adaptive Personalized Explainability](https://mlsanity.com/paper/2504.13856/) (2025)
- [DISTINQT: A Distributed Privacy Aware Learning Framework for QoS Prediction for Future Mobile and Wireless Networks](https://mlsanity.com/paper/2401.10158/) (2024)
- [FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings](https://mlsanity.com/paper/2210.03766/) (2022)

## Abstract (arXiv)

This paper proposes a novel split learning framework with multiple end-systems in order to realize privacypreserving deep neural network computation. In conventional split learning frameworks, deep neural network computation is separated into multiple computing systems for hiding entire network architectures. In our proposed framework, multiple computing end-systems are sharing one centralized server in split learning computation, where the multiple end-systems are with input and first hidden layers and the centralized server is with the other hidden layers and output layer. This framework, which is called as spatio-temporal split learning, is spatially separated for gathering data from multiple end-systems and also temporally separated due to the nature of split learning. Our performance evaluation verifies that our proposed framework shows nearoptimal accuracy while preserving data privacy.