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一个高效的集团联合学习框架,用于大规模基于EEG的驾驶员昏昏欲睡的检测.

Xinyuan Chen1, Yi Niu1, Yanna Zhao1

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China.

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概括
此摘要是机器生成的。

集团联合学习 (Group-FL) 通过使用电脑电图 (EEG) 信号来增强驾驶员昏昏欲睡的检测. 这种保护隐私的方法可以提高数据的利用率和准确性,从而提高道路的安全性.

关键词:
电脑脑电图 (EEG) 是一种电脑电图.深度学习是一种深度学习.司机昏昏欲睡的检测检测 司机昏昏欲睡的检测联合学习 (FL)

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 运输安全运输安全

背景情况:

  • 司机昏昏欲睡是交通事故的主要原因之一.
  • 监测电脑电图 (EEG) 信号为检测昏昏欲睡提供了一个有效的解决方案.
  • 现有的方法面临着数据隐私和数据利用不足的挑战.

研究的目的:

  • 提出一个集团联合学习 (Group-FL) 框架,用于大规模,保护隐私的驾驶员昏昏欲睡的检测.
  • 解决驾驶员监控系统中的数据异质性和隐私问题.
  • 通过使用分布式EEG数据来提高昏昏欲睡检测的效率和有效性.

主要方法:

  • 开发了一个集团联合学习 (Group-FL) 框架,将客户组织成等级组,以实现高效的聚合.
  • 一个全球个性化的深度神经网络被设计用于从各种EEG数据中提取共享和细粒度的特征.
  • 实施了三个检查模块来处理数据不平衡,污染和个性化模型应用.

主要成果:

  • 集团-FL框架证明了有效利用各种客户数据,同时保持隐私.
  • 全球个性化深度神经网络的平均精度为81.0%,F1得分为82.0%,AUC为87.9%.
  • 实验验证证了框架内的单个组件的有效性.

结论:

  • 拟议的Group-FL框架为大规模的驾驶员昏昏欲睡检测提供了一种高效且保护隐私的解决方案.
  • 全球个性化的深度神经网络有效地处理不同客户端的EEG信号变化.
  • 这种方法显著提升了基于EEG的驾驶员监控系统在现实世界中应用的潜力.