Security and Resilience in Distributed Machine Learning

Overview of Federated Learning

This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning.

Challenges in FL

This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrity.

Defense Mechanisms

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.

Emerging Trends in FL

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.

Applications and Importance

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.

Target Audience and Contribution

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.