# LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data

arXiv: https://arxiv.org/abs/2203.04305 · HTML: https://mlsanity.com/paper/2203.04305/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [NoPeek: Information leakage reduction to share activations in distributed deep learning](https://mlsanity.com/paper/2008.09161/) (2020)
- [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://mlsanity.com/paper/2003.13376/) (2020)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [No Peek: A Survey of private distributed deep learning](https://mlsanity.com/paper/1812.03288/) (2018)
- [FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks](https://mlsanity.com/paper/2011.03180/) (2020)
- ["The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering](https://mlsanity.com/paper/1912.10858/) (2019)
- [A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification](https://mlsanity.com/paper/1810.07088/) (2018)

## Built on by (most cited first, 5 of 5)

- [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)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environments](https://mlsanity.com/paper/2407.02693/) (2024)
- [Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting](https://mlsanity.com/paper/2403.01438/) (2024)
- [Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices](https://mlsanity.com/paper/2502.08692/) (2025)

## Abstract (arXiv)

Federated learning (FL) and split learning (SL) are the two popular distributed machine learning (ML) approaches that provide some data privacy protection mechanisms. In the time-series classification problem, many researchers typically use 1D convolutional neural networks (1DCNNs) based on the SL approach with a single client to reduce the computational overhead at the client-side while still preserving data privacy. Another method, recurrent neural network (RNN), is utilized on sequentially partitioned data where segments of multiple-segment sequential data are distributed across various clients. However, to the best of our knowledge, it is still not much work done in SL with long short-term memory (LSTM) network, even the LSTM network is practically effective in processing time-series data. In this work, we propose a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to classify time-series data with multiple clients. The differential privacy (DP) is applied to solve the data privacy leakage. The proposed method, LSTMSPLIT, has achieved better or reasonable accuracy compared to the Split-1DCNN method using the electrocardiogram dataset and the human activity recognition dataset. Furthermore, the proposed method, LSTMSPLIT, can also achieve good accuracy after applying differential privacy to preserve the user privacy of the cut layer of the LSTMSPLIT.