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Visualizing Visual Adaptation
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Low-rank adaptation for edge AI.

Zhixue Wang1, Hongyao Ma2, Jiahui Zhai2

  • 1Shandong Jiaotong University, Haitang Road 5001, 250357, Jinan, China. zhixue.w@163.com.

Scientific Reports
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Low-Rank Adaptation for Edge AI (LoRAE) efficiently updates artificial intelligence (AI) models on resource-constrained edge devices. This method significantly reduces trainable parameters while maintaining high model accuracy for various AI tasks.

Keywords:
Edge AILow-rank adaptationModel update efficiencyParameter reduction

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision
  • Edge Computing

Background:

  • Edge artificial intelligence (AI) offers transformative applications but faces challenges in updating models on resource-limited devices.
  • Updating models on edge devices is hampered by constraints in computation and communication capabilities.
  • Existing methods for model updates often require substantial computational and communication resources, making them unsuitable for edge environments.

Purpose of the Study:

  • To introduce a novel method, Low-Rank Adaptation for Edge AI (LoRAE), designed to address the challenges of updating AI models on edge devices.
  • To significantly reduce the number of trainable parameters required for model updates, thereby minimizing computational and communication overhead.
  • To evaluate the effectiveness of LoRAE in maintaining or improving model accuracy across diverse AI tasks on edge devices.

Main Methods:

  • LoRAE utilizes low-rank decomposition of convolutional neural network (CNN) weight matrices to decrease the number of updated parameters.
  • The method reduces the trainable parameters to approximately 4% compared to traditional full-parameter updates.
  • Experiments were conducted on image classification, object detection, and image segmentation tasks using the YOLOv8x model.

Main Results:

  • LoRAE achieved substantial parameter reductions: 86.1% for image classification, 98.6% for object detection, and 94.1% for image segmentation.
  • These significant reductions in trainable parameters were achieved without compromising, and in some cases enhancing, model accuracy.
  • The method effectively mitigates computational and communication challenges associated with updating AI models on edge devices.

Conclusions:

  • LoRAE presents a highly efficient and precise solution for updating AI models in resource-constrained edge environments.
  • The method demonstrates the feasibility of deploying advanced AI capabilities on edge devices through optimized model updates.
  • LoRAE holds significant potential for advancing the practical implementation of edge AI across various applications.