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Related Experiment Video

Updated: Nov 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

630

A Partition Based Gradient Compression Algorithm for Distributed Training in AIoT.

Bingjun Guo1, Yazhi Liu1, Chunyang Zhang1

  • 1Department of Computer Science and Technology, North China University of Science and Technology, Tangshan 063210, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive compression strategy for distributed Deep Neural Networks (DNNs) training on Internet of Things (IoT) devices. The method significantly reduces communication overhead by partitioning and filtering gradients, enhancing training efficiency.

Keywords:
AIoTdistributed traininggradient compressiontraining efficiency

Related Experiment Videos

Last Updated: Nov 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

630

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Distributed Deep Neural Networks (DNNs) on Internet of Things (IoT) nodes offer enhanced performance.
  • Limited resources in IoT nodes create communication efficiency challenges for distributed DNN training.

Purpose of the Study:

  • To propose an adaptive compression strategy to mitigate high communication overhead in distributed DNN training on IoT nodes.

Main Methods:

  • A neural network predicts gradient distribution for parameter partitioning into key and sparse regions.
  • Information entropy determines a threshold to filter gradient values, transmitting only significant ones.

Main Results:

  • The adaptive compression strategy achieves a maximum compression ratio of 37.1 times.
  • This approach effectively reduces traffic and improves distributed training efficiency.

Conclusions:

  • Gradient partitioning and adaptive thresholding offer an effective solution for communication bottlenecks in distributed DNNs on IoT.
  • The proposed strategy enhances the feasibility of deploying complex DNNs on resource-constrained IoT devices.