Übersicht			Auf mobile öffnen

Lieferstatus:	i.d.R. innert 7-14 Tagen versandfertig
Veröffentlichung:	Mai 2026
Genre:	EDV / Informatik
	AI Security and Robustness / Computer Communication Networks / Data Privacy in AI / Datenschutz / Federated Learning Attacks / Federated Learning Defenses / federated learning security / Hardware Performance and Reliability / machine learning / Maschinelles Lernen / Mobile and Network Security / Netzwerk-Hardware / Netzwerksicherheit / Privacy / Reliable Federated Learning / Resilient Federated Learning mehr...
ISBN:	9783032239587
EAN-Code:	9783032239587
Verlag:	Springer International Publishing
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D
Seiten:	238
Illustration:	XX, 238 p. 75 illus., 73 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrity. Through cutting-edge research and practical insights, the book introduces defense mechanisms like representational similarity analysis and visual explanation techniques, which safeguard FL models while ensuring performance and scalability. It also explores the evolving landscape of FL, including the integration of graph neural networks, explainable AI, and energy-efficient designs that drive sustainability in distributed systems. As FL becomes increasingly vital across industries—from healthcare and finance to IoT and smart cities—this book addresses the critical balance between security, functionality, and compliance with global data privacy regulations. It is an essential resource for researchers, industry professionals, and policymakers aiming to navigate and contribute to the rapidly growing domain of FL. By bridging theory and practice, this book contributes to advancing secure and resilient FL technologies.