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修改器引导的弹性CNN推断使物联网的容错边缘协作成为可能
Omid Jamshidi1, Mahdi Abbasi2,3,4, Abbas Ramazani5
1Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, 6516738695, Iran.
Scientific reports
|November 27, 2025
概括
本研究介绍了一种基于边缘的深度学习系统,用于物联网 (IoT). 它即使在设备故障的情况下也确保了准确的,容错的推断,消除了对云的依赖.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 物联网的物联网,就是物联网.
背景情况:
- 资源有限的物联网 (IoT) 环境面临着由于设备故障,有限的计算能力和隐私问题而导致深度学习推断的挑战.
- 基于云计算的解决方案通常不适合物联网,因为这些局限性和实时本地化处理的需要.
研究的目的:
- 开发一种具有弹性,完全基于边缘的分布式卷积神经网络 (CNN) 架构,用于在物联网中准确和容错的深度学习推断.
- 为了消除云计算的依赖性,同时保持高性能和对设备故障的稳定性.
主要方法:
- 一个轻量级的修改器模块被开发并部署在边缘,通过将来自同行CNN的输出和权重组合起来,合成对故障设备的预测.
- 采用了一种新的故障模拟技术来训练修改器模块,以便在没有模型重复或云备用的情况下实时模拟缺失的输出.
- 该方法使用MNIST和CIFAR-10数据集在各种数据分区场景下进行评估,模拟多达五个同时发生的设备故障.
主要成果:
- 拟议的系统显示了高达1.5%的绝对精度改进和30%的错误率减少.
- 该系统保持稳定的运行,设备机率超过80%,表现优于集体,机和联合学习基线.
- 该解决方案表现出低资源利用率 (每个模型大约15KB) 和实时响应能力.
结论:
- 开发的基于边缘的分布式CNN架构为资源受限的物联网场景中深度学习推断提供了强大的,准确的和容错的解决方案.
- 该系统能够在没有云依赖的情况下运行,加上其低资源利用率和对设备故障的弹性,使其成为安全关键物联网应用的理想选择.
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