适应性联合学习用于资源有限的物联网设备,通过边缘智能和多边缘集群
Fahad Razaque Mughal1, Jingsha He1, Bhagwan Das2
1Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Scientific reports
|November 20, 2024
概括
物联网边缘计算中的联合学习 (FL) 在节点选择和资源分配方面面临挑战. 我们新的多边缘集群和边缘AI异质联合学习 (MEC-AI HetFL) 架构显著提高了性能和准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 对物联网 (IoT) 边缘计算至关重要,它提供了可扩展性和低能耗.
- 不同质的边缘环境在有效的边缘节点选择和资源分配方面存在挑战.
- 动态和资源有限的设置加剧了这些困难,影响了系统的整体性能.
研究的目的:
- 提出一种新的架构,MEC-AI HetFL,用于异质边缘计算中的高效联合学习.
- 解决物联网环境中动态节点选择和资源优化的挑战.
- 提高边缘AI节点的协作和学习效率.
主要方法:
- 开发了多边缘集群和边缘AI异质联合学习 (MEC-AI HetFL) 架构.
- 实现多边缘聚类和人工智能驱动的节点通信,用于动态节点选择.
- 优化全球学习任务,重点关注低复杂性和高效的资源配置.
主要成果:
- 与现有方法 (EdgeFed,FedSA,FedMP,H-DDPG) 相比,MEC-AI HetFL在资源分配,质量评分和学习准确性方面表现出卓越的表现.
- 在异质和分布式环境中实现高达5倍的更好的性能.
- 通过模拟和网络流量测试进行验证,证实了物联网边缘部署的有效性.
结论:
- MEC-AI HetFL有效地解决了物联网边缘计算的关键挑战,特别是在动态和资源有限的场景中.
- 拟议的架构为异质边缘环境的联合学习提供了重大进展.
- 由人工智能驱动的方法增强了协作学习和资源管理,以提高物联网系统性能.
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