FEDERATED LEARNING PRIVACY-PRESERVING MACHINE LEARNING IN DISTRIBUTED SYSTEMS
<p>"Federated Learning: Privacy-Preserving Machine Learning in Distributed Systems" explores the innovative concept of federated learning, which enables machine learning models to be trained across decentralized devices or servers while keeping data localized on the devices, ensuring privacy preservation. The book delves into the technical aspects of federated learning, discussing how it allows for collaborative training of models without the need to share sensitive data. It covers various techniques, challenges, and solutions related to privacy, security, and data governance, providing readers with a comprehensive understanding of federated learning's applications in distributed systems, including healthcare, finance, and IoT. The book is ideal for researchers, data scientists, and practitioners interested in privacy-preserving machine learning and decentralized AI systems.</p>