通过基于基因算法的优化基础架构的基因算法来加强联合学习中的体育图像数据分类
De Sheng Fu1, Jie Huang2, Dibyanarayan Hazra3
1College of Public Education, ZheJiang Institute of Economics and Trade HangZhou, ZheJiang, China.
PloS one
|July 11, 2024
概括
联合学习优化了使用遗传算法的模型,以更快地推断和减少存储. 这种方法增强了对资源有限的设备的部署,通过深度学习架构实现了高精度.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行分散的模型培训.
- 传统的FL是客户端模型权重的平均值,可能会限制优化.
- 资源有限的设备对部署复杂的深度学习模型构成挑战.
研究的目的:
- 通过使用遗传算法 (GA) 引入一种用于优化联合学习基础模型的新方法.
- 改善推断时间,减少FL模型的存储要求.
- 为了使FL模型能够在资源有限的硬件上有效地部署.
主要方法:
- 开发了一种遗传算法方法,用于优化联合学习环境中的基础模型.
- 关键的GA操作,如染色体表示,交叉和突变,详细说明了例子.
- 该方法使用体育数据集在不同客户数量的平衡和不平衡场景中进行了测试.
- 四个深度学习架构 (AlexNet,VGG19,ResNet50,EfficientNetB3) 被用作基础模型.
主要成果:
- 基于GA的方法显著改善了推断时间,减少了存储空间.
- 在平衡的数据集上使用EfficientNetB3与9个客户端实现了92.34%的准确性.
- 优化EfficientNetB3的结果是推断时间提高了20%,存储空间减少了2.35%.
结论:
- 遗传算法为优化联合学习模型提供了一种有效的策略.
- 拟议的方法提高了模型的效率,使它们适用于资源有限的环境.
- 这项研究证明了将GA集成到联合深度学习架构中的实际好处.
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