为动态边缘提供联合学习:一种模块化和弹性方法
Leonardo Almeida1,2, Rafael Teixeira1,2, Gabriele Baldoni3
1Intituto de Telecomunicações, 3810-193 Aveiro, Portugal.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了边缘计算的弹性联合学习 (FL) 框架. 模块化设计提高了故障耐受性和通信效率,而Zenoh被证明是最有效的.
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:
- 在边缘环境中,对分布式机器学习 (ML) 的需求不断增长.
- 对于联合学习 (FL) 的容错性,弹性和通信效率的挑战.
- 在动态,资源有限的环境中需要适应性框架.
研究的目的:
- 提出一个新的模块化和弹性FL框架.
- 解决边缘ML中故障耐受性和通信效率的挑战.
- 提高FL系统对各种环境的适应性.
主要方法:
- 开发了一个模块化的FL框架,其中核心功能的解.
- 集成了各种通信协议 (Zenoh,MQTT,Kafka) 和FL范式.
- 模拟的概率工人未能测试弹性和性能.
主要成果:
- 框架在整合多种通信协议和FL方法方面表现出灵活性.
- 协议选择对性能产生重大影响,特别是在大量通信中.
- 在测试的协议中,Zenoh显示了最低的开销和最高的效率.
- 尽管模拟失败,但模型训练实现了0.9453的马修斯相关系数 (MCC) 的趋同.
结论:
- 拟议的模块化框架提高了联合学习的弹性和效率.
- 优化的通信协议选择对于边缘环境中的性能至关重要.
- 该框架有效地在破坏性条件下维持FL运营,确保模式的趋同.
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