Isbn: 9783032239587 - security and resilience in distributed machine learning: challenges, techniques, and future directions (springer series in reliability engineering) (12 resultados)

Condición: New.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. …

Condición: New.

Hardcover. Condición: Brand New. 258 pages. 6.14x0.63x9.21 inches. In Stock.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Hardcover. Condición: new. Hardcover. 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 industriesfrom healthcare and finance to IoT and smart citiesthis 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. This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 238 pp. Englisch.…

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 industriesfrom healthcare and finance to IoT and smart citiesthis 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 260 pp. Englisch.…

Hardcover. Condición: new. Hardcover. 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 industriesfrom healthcare and finance to IoT and smart citiesthis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condición: new. Hardcover. 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 industriesfrom healthcare and finance to IoT and smart citiesthis 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. This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.