Security and Resilience in Distributed Machine Learning: Challenges, Techniques, and Future Directions

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.