基于MAB的在线客户端调度,用于物联网中的分散式联合学习
Zhenning Chen1, Xinyu Zhang2, Siyang Wang3,4
1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
物联网中的分散式联合学习 (DFL) 面临由于设备异质性的调度挑战. 这项研究提出了一种使用多武装强盗的在线学习算法,以优化客户端选择并减少系统延迟.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 无线通信无线通信
背景情况:
- 传统的联合学习 (FL) 依赖于中央服务器,限制了可扩展性和稳定性.
- 分散的联合学习 (DFL) 通过在边缘服务器之间实现对等模型交换来增强FL.
- 在物联网 (IoT) 中部署DFL受到有限的无线资源和需要高效的客户端调度的阻碍.
研究的目的:
- 在没有事先客户端信息的情况下,为物联网环境的DFL解决客户端调度和资源优化的挑战.
- 开发一种能够准确估计客户参与延迟的在线学习算法,尽管有异质资源和时间变化的无线通道.
- 通过优化客户选择,提高DFL系统的融合率和模型准确性.
主要方法:
- 将客户端调度和资源优化问题重新构建为一个多武装强盗 (MAB) 程序.
- 提出了一种在线学习算法,使用上下文多臂老虎机进行延迟估计和客户调度.
- 进行理论分析以确定拟议算法的非对称最佳性.
主要成果:
- 拟议的算法在理论分析中实现了非对称的最佳性能.
- 实验结果表明,该算法能够进行非对称的最佳客户端选择.
- 该方法在减少累积系统延迟方面明显优于现有的算法.
结论:
- 开发的在线学习算法有效地解决了客户端调度和资源优化在DFL的物联网.
- 准确的客户端延迟估计和调度对于在资源有限的环境中高效地部署DFL至关重要.
- 这种方法提供了一种优越的解决方案,通过最大限度地减少全系统延迟来提高DFL性能.
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