在车辆的联合学习辅助互联网中多目标分布式客户端选择
1The School of Computer and Information Management, Inner Mongolia University of Finance and Economics, Huhort 010051, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了用于汽车互联网 (IoV) 的联合学习 (FL) 的分布式客户端选择方法. 它通过选择高评价的客户来降低成本,大大降低了通信开支.
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) 是一种分布式机器学习框架,在车辆互联网 (IoV) 中越来越多地使用.
- 数以百万计的车辆参与FL训练模型,但由于参与者数量众多,维持活跃状态需要付出大量成本.
- 在IoV联合学习中需要有效的客户端选择,这对于管理计算和通信开销至关重要.
研究的目的:
- 提出一个分布式客户端选择方案,用于汽车互联网中的联合学习.
- 为了降低与保持大量参与车辆的活跃状态相关的成本.
- 开发一个有效的客户评估机制,平衡多个关键变量.
主要方法:
- 提出了一个分布式客户选择方案,客户根据他们的评价在邻居中被选中.
- 评估人员考虑四个关键变量:样本数量,可用的吞吐量,计算能力和本地数据集质量.
- 模糊逻辑被采用为评估方法,这是因为多变量问题的闭式解决方案缺乏.
主要成果:
- 拟议的分布式客户端选择方案有效地降低了参与者维持活跃状态的成本.
- 该方法接近于中央集中的客户选择方法的准确性.
- 广泛的模拟表明,通信开销大幅减少.
结论:
- 开发的分布式客户端选择方案对车辆互联网中的联合学习有效.
- 模糊逻辑为基于多个复杂变量的客户评估提供了一个强大的方法.
- 这些发现表明,在大型车辆网络中优化联合学习的实际解决方案.
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