Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 12 Feb 2025.
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17 of 36 references.
References · 17
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
- 2018Distributed learning of deep neural network over multiple agentscited by 277
- 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
- 2021Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillationcited by 131
- 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 104
- 2020Compressing Large-Scale Transformer-Based Models: A Case Study on BERTcited by 95
- 2019BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systemscited by 94
- 2020Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4MLcited by 42
- 2020Distributed Training of Deep Learning Models: A Taxonomic Perspectivecited by 25
- 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
5 more
- 2021Quantization and Deployment of Deep Neural Networks on Microcontrollerscited by 20
- 2021Neural Network Quantization for Efficient Inference: A Surveycited by 13
- 2022LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Datacited by 5
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
- 2024UAV-assisted Distributed Learning for Environmental Monitoring in Rural Environmentscited by 3
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