工业物联网中的高效协作学习使用联邦式学习和基于Shapley值的自适应权重
Dost Muhammad Saqib Bhatti1, Mazhar Ali1, Junyong Yoon1
1School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了Shapley基于价值的工业物联网 (IIoT) 联合学习 (FL) 方法,以提高AI模型的准确性和效率. 适应权重机制通过考虑数据多样性和降低计算成本来增强全球模型培训.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 工业物联网的工业物联网.
背景情况:
- 联合学习 (FL) 能够在保护数据隐私的同时进行协作AI模型培训.
- 跨行业的数据多样性可能会阻碍FL全球模型培训的有效性.
- 工业物联网 (IIoT) 集成为工业4.0中的安全,协作AI提供了潜力.
研究的目的:
- 增强国际工业物联网联合学习全球模式培训的稳定性和准确性.
- 解决数据多样性影响联合学习模型性能的挑战.
- 为了减少与联合学习相关的计算开销.
主要方法:
- 为全球模型培训提出了基于Shapley价值的自适应权重机制.
- 将全球模型训练为一系列合作游戏,根据Shapley贡献,数据集大小和可变性调整客户权重.
- 引入了一种量子化策略,以减轻沙普利值计算的计算成本.
主要成果:
- 由于有效的重量分配,与现有方法相比,获得了最高的准确性.
- 证明了可比的准确性,计算成本显著降低.
- 在每个训练轮中减少了Shapley值计算的计算开销.
结论:
- 拟议的Shapley基于价值的自适应权重机制提高了IIoT联合学习的全球模型性能.
- 该方法有效地平衡了准确性和计算效率.
- 这种方法为工业4.0环境中安全和协作的人工智能提供了一个有希望的解决方案.
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