# Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices

arXiv: https://arxiv.org/abs/2502.08692 · HTML: https://mlsanity.com/paper/2502.08692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation](https://mlsanity.com/paper/2105.08919/) (2021)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [Compressing Large-Scale Transformer-Based Models: A Case Study on BERT](https://mlsanity.com/paper/2002.11985/) (2020)
- [BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://mlsanity.com/paper/1910.14315/) (2019)
- [Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML](https://mlsanity.com/paper/2003.06308/) (2020)
- [Distributed Training of Deep Learning Models: A Taxonomic Perspective](https://mlsanity.com/paper/2007.03970/) (2020)
- [FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks](https://mlsanity.com/paper/2011.03180/) (2020)
- [Quantization and Deployment of Deep Neural Networks on Microcontrollers](https://mlsanity.com/paper/2105.13331/) (2021)
- [Neural Network Quantization for Efficient Inference: A Survey](https://mlsanity.com/paper/2112.06126/) (2021)
- [LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data](https://mlsanity.com/paper/2203.04305/) (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)

## Built on by (most cited first, 1 of 1)

- [Split Learning-Enabled Framework for Secure and Light-weight Internet of Medical Things Systems](https://mlsanity.com/paper/2511.00336/) (2025)

## Abstract (arXiv)

Split Learning (SL) recently emerged as an efficient paradigm for distributed Machine Learning (ML) suitable for the Internet Of Things (IoT)-Cloud systems. However, deploying SL on resource-constrained edge IoT platforms poses a significant challenge in terms of balancing the model performance against the processing, memory, and energy resources. In this work, we present a practical study of deploying SL framework on a real-world Field-Programmable Gate Array (FPGA)-based edge IoT platform. We address the SL framework applied to a time-series processing model based on Recurrent Neural Networks (RNNs). Set in the context of river water quality monitoring and using real-world data, we train, optimize, and deploy a Long Short-Term Memory (LSTM) model on a given edge IoT FPGA platform in different SL configurations. Our results demonstrate the importance of aligning design choices with specific application requirements, whether it is maximizing speed, minimizing power, or optimizing for resource constraints.