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一个与大小内核注意力网络的联合学习,用于图像分类

Tianzhe Liu¹, Jing Xie², Heng Dong³

¹Fujian Police College, Fuzhou, China.

Frontiers in plant science

|March 9, 2026

概括

本研究介绍了FL-LSNet,一个使用大小网络 (LSNet) 的联合学习 (FL) 框架,以提高协作学习中的数据安全性和性能. FL-LSNet 提高了准确性,并减少了各种应用的计算负载.

科学领域:

计算机科学计算机科学
人工智能的人工智能
机器学习机器学习

背景情况:

联合学习 (FL) 面临异质图像数据的挑战,影响安全,隐私和性能.
现有的FL框架在复杂的图像特性和平衡协作与数据安全方面扎.

研究的目的:

介绍FL-LSNet,这是一个新的联合学习框架,具有轻量级的大小网络 (LSNet).
在协作图像学习中解决数据安全,隐私和性能退化问题.

主要方法:

开发了FL-LSNet,采用客户端-服务器架构来实现分散的预处理和数据隐私.
集成的LSNet,用于全球环境的大型内核感知器 (LKP) 和用于本地融合的小型内核注意力 (SKA).
实现了对长尾数据和服务器端聚合的动态权重调整.

主要成果:

与Swin变压器和基线模型相比,LSNet减少了7%的计算开销,并提高了19%的功能表示.
在三个数据集上,FL-LSNet的表现优于FedAvg和MOON,准确度达到84.32%至98.92%.
废弃性研究表明,FedAvg-LSNet集成超过了基线6.15%.

结论:

在联合学习中,FL-LSNet提供了一个可扩展的解决方案,用于多利益相关方的数据协作.
介绍了FL用于公共安全,农业和医学诊断的轻量级垂直适应的新见解.

关键词:

大规模的内核注意事项注意力网络注意力网络联合学习的联合学习图像的分类图像的分类.轻量级的轻量级的轻量级的轻量级的

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