Adversarial AI and Data Poisoning in Federated Learning

Vipul Jain

ISBN 13: 9798337362243

Editore: IGI GLOBAL SCIENTIFIC PUBLISHING Feb 2026, 2026

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Neuware - 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.

Codice articolo 9798337362243

Dati bibliografici

• Titolo: Adversarial AI and Data Poisoning in Federated Learning
• Editore: IGI GLOBAL SCIENTIFIC PUBLISHING Feb 2026
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798337362243
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 286 L x 221 A x 35 P (millimetri) 286 millimetri di larghezza per 221 millimetri di altezza per 35 millimetri di profondità
• Peso: 1685 grammi

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Riassunto

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.