使用物联网应用程序进行智能通信的联合学习

English | 2024 | ISBN: 9781003489368 | 274 pages | True PDF,EPUB | 12.81 MB

The effectiveness of federated learning in high‑performance information systems and informatics‑based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‑based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.

Features

• Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy. • Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy. • Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area. • Analyses the need for a personalized federated learning framework in cloud‑edge and wireless‑edge architecture for intelligent IoT applications. • Comprises real‑life case illustrations and examples to help consolidate understanding of topics presented in each chapter.

This book is recommended for anyone interested in federated learning‑based intelligent algorithms for smart communications.

英文| 2024 |国际标准图书编号：9781003489368 | 274页|真PDF，EPUB | 12.81 MB 本书展示了联合学习在高性能信息系统和基于信息学的解决方案中的有效性，以满足当前的信息支持需求。为了应对物联网（IoT）环境中的异构性挑战，使用IoT应用程序的智能通信联合学习分析了能够减轻异构性在多个维度上的不利后果的个性化联合学习算法的发展。它包括基于物联网的人类活动识别的案例研究，以展示个性化联合学习对智能物联网应用的有效性。 特征 •演示了联邦学习如何提供一种新颖的方法，在不侵犯用户隐私的情况下，从数据中构建个性化模型。•描述联合学习如何在保护用户隐私的同时，帮助理解和学习物联网应用程序中的用户行为对当前关于联合学习的研究进行了详细分析，为读者提供了对该领域的广泛理解分析智能物联网应用程序在云边缘和无线边缘架构中对个性化联邦学习框架的需求包括现实生活中的案例说明和例子，以帮助巩固对每一章中主题的理解。 本书推荐给任何对基于联邦学习的智能通信算法感兴趣的人。

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