基于物联网的联合学习的全面概述:专注于客户选择方法
Naghmeh Khajehali1, Jun Yan1, Yang-Wai Chow1
1School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
联合学习 (FL) 能够在物联网 (IoT) 设备上实现协作机器学习 (ML). 本综述详细介绍了客户端选择挑战和在动态物联网环境中有效的FL的方法.
科学领域:
- * 物联网 (IoT) 和机器学习 (ML) 的整合.
- * 联邦学习 (FL) 用于分散的数据处理.
- * 在动态,资源有限的环境中优化ML模型.
背景情况:
- * 传统的集中式机器学习面临着巨大的物联网数据的可扩展性和隐私问题.
- *联合学习 (FL) 训练模型以合作方式使用参数,而不是原始数据.
- *物联网客户端的异质性 (计算,通信,网络,数据质量) 给FL带来了重大挑战.
研究的目的:
- * 进行一项系统的文献审查 (SLR) 关于客户选择 (CS) 挑战在FL.
- *为各种应用提供了CS过程及其特征的全面概述.
- *对物联网中的FL进行现有的CS方法进行分类和解释.
主要方法:
- * 系统文献审查 (SLR) 方法.
- * 在联邦学习 (FL) 中分析客户选择 (CS) 过程.
- * 基于特征和挑战减轻的CS方法的分类.
主要成果:
- *鉴定了FL客户端选择中的关键挑战,原因是物联网设备异质性和动态环境.
- * 提供了对 CS 的抽象实现和基本特征的结构化概述.
- * 分类了各种CS方法,突出了它们在应对特定FL挑战方面的优势.
结论:
- *有效的客户端选择对于物联网中高质量的联合学习至关重要.
- * 本综述提供了关于佛罗里达州科学科学研究现状的见解.
- * 提供了未来研究和开发FL先进的CS方法的路线图.
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