‘Essential Federated Learning: AI at the Edge’ offers a comprehensive exploration into the transformative domain of federated learning, an innovative approach reshaping the AI landscape by enabling data decentralization. This book demystifies the foundational concepts of federated learning, capturing its potential to increase privacy, enhance data security, and empower industries across sectors such as healthcare, finance, and beyond. By keeping data localized, federated learning minimizes privacy concerns while leveraging the power and capability of edge computing. Each chapter meticulously builds upon the last, guiding readers from basic principles to advanced applications, providing a balanced understanding of technical architectures, algorithms, and real-world implementations.
Rich with insights into the ethical and social implications of federated learning, this book addresses the pressing challenges and future directions that are critical for its evolution. Topics such as privacy preservation, bias mitigation, and regulatory compliance are thoroughly examined, offering a holistic view of how federated learning can be applied responsibly and effectively. Whether you’re a researcher, practitioner, or policy-maker, ‘Essential Federated Learning: AI at the Edge’ offers the essential knowledge needed to harness the advantages of this cutting-edge technology, ensuring readers are well-equipped to navigate the rapidly expanding landscape of AI and edge computing.
Robert Johnson
Essential Federated Learning [EPUB ebook]
AI at the Edge
Essential Federated Learning [EPUB ebook]
AI at the Edge
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Língua Inglês ● Formato EPUB ● Páginas 308 ● ISBN 6610000663170 ● Tamanho do arquivo 0.9 MB ● Editora HiTeX Press ● Publicado 2024 ● Carregável 24 meses ● Moeda EUR ● ID 10005770 ● Proteção contra cópia sem