Isbn: 9798337362250 - adversarial ai and data poisoning in federated learning

Paperback. Condición: new. Paperback. With the growing security challenges at the intersection of distributed machine learning and malicious interference, there are growing challenges that federated learning can address. Federated learning enables collaborative model training across devices while preserving data privacy. However, this decentralized nature also opens new vulnerabilities, particularly to adversarial attacks and data poisoning, where malicious actors can inject corrupted data or manipulate updates to degrade models or extract sensitive information. As the adoption of federated learning accelerates, understanding and these threats are essential to ensure model integrity and resilience in real-world situations. Adversarial AI and Data Poisoning in Federated Learning provides a comprehensive examination of emerging threats, attack vectors, and defense mechanisms within federal learning systems. This book highlights vulnerabilities of federated learning architectures, explores strategies for detection and mitigation of adversarial threats, and presents real-world case studies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Adversarial AI and Data Poisoning in Federated Learning

Vipul Jain (u. a.)

Idioma: Inglés

Editorial: IGI GLOBAL SCIENTIFIC PUBLISHING, 2026

9798337362250

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Librería: preigu, Osnabrück, Alemaniapreigu

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EUR 301,05

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Adversarial AI and Data Poisoning in Federated Learning

Manju Lata Joshi

Idioma: Inglés

Editorial: IGI GLOBAL SCIENTIFIC PUBLISHING, 2026

9798337362250

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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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Condición: Nuevo

EUR 472,90

Envío por EUR 40,47

Se envía de Alemania a Estados Unidos de America

Cantidad disponible: 2 disponibles

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the growing security challenges at the intersection of distributed machine learning and malicious interference, there are growing challenges that federated learning can address. Federated learning enables collaborative model training across devices while preserving data privacy. However, this decentralized nature also opens new vulnerabilities, particularly to adversarial attacks and data poisoning, where malicious actors can inject corrupted data or manipulate updates to degrade models or extract sensitive information. As the adoption of federated learning accelerates, understanding and these threats are essential to ensure model integrity and resilience in real-world situations. Adversarial AI and Data Poisoning in Federated Learning provides a comprehensive examination of emerging threats, attack vectors, and defense mechanisms within federal learning systems. This book highlights vulnerabilities of federated learning architectures, explores strategies for detection and mitigation of adversarial threats, and presents real-world case studies.…

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Adversarial AI and Data Poisoning in Federated Learning