Inhaltsangabe
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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Über die Autorinnen und Autoren
Dr. Shikha Khullar is an accomplished academic and researcher with more than 14 years of experience in computer science and emerging technologies. Her expertise lies in artificial intelligence, computational intelligence, and their applications in areas such as fraud detection, intelligent agriculture, cloud security, federated learning, and blockchain. She has authored and contributed to several international publications and book chapters with reputed publishers including IGI Global and Wiley–Scrivener. Alongside her research, Dr. Khullar is deeply committed to teaching and mentoring, guiding students to apply technology for real-world problem-solving. Her vision emphasizes innovation, sustainability, and bridging the gap between academia and industry.
Dr. Manju Lata Joshi holds an M.Tech. and a Doctorate in Computer Science from Banasthali Vidyapith. My current research interests encompass Artificial Intelligence, Natural Language Processing, Information Retrieval, and Text Mining. With over 17 years of experience in teaching and research, I have published numerous research papers in prestigious journals indexed in the Science Citation Index (SCI) and Scopus, both nationally and internationally. Furthermore, I serve on the review and advisory committees of several refereed journals and conferences.
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers 9798337362250
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Taschenbuch. Zustand: Neu. Adversarial AI and Data Poisoning in Federated Learning | Vipul Jain (u. a.) | Taschenbuch | Englisch | 2026 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337362250 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 134617358
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