Name: Security and Resilience in Distributed Machine Learning: Challenges, Techniques, and Future Directions (en Inglés)
SKU: 67101005
Price: 188510.00 CLP
Availability: InStock
ISBN: 9783032239587

portada Security and Resilience in Distributed Machine Learning: Challenges, Techniques, and Future Directions (en Inglés)

Security and Resilience in Distributed Machine Learning: Challenges, Techniques, and Future Directions (en Inglés)

Formato

Libro Físico

Editorial

Springer, Berlin

Año

2026

Idioma

Inglés

Encuadernación

Tapa Dura

Dimensiones

23.5x15.5 cm

ISBN13

9783032239587

Security and Resilience in Distributed Machine Learning: Challenges, Techniques, and Future Directions (en Inglés)

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Reseña del libro "Security and Resilience in Distributed Machine Learning: Challenges, Techniques, and Future Directions (en Inglés)"

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.