federated learning theory practice (5 Ergebnisse)

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - How can we train powerful machine learning models together across smartphones, hospitals, or financial institutions without ever sharing raw data This book delivers a compelling answer through the lens of federated learning (FL), a cutting-edge paradigm for decentralized, privacy-preserving machine learning. Designed for students, engineers, and researchers, this book offers a principled yet practical roadmap to building secure, scalable, and trustworthy FL systems from scratch.At the heart of this book is a unifying framework that treats FL as a network-regularized optimization problem. This elegant formulation allows readers to seamlessly address personalization, robustness, and fairness challenges often tackled in isolation. You ll learn how to structure FL networks based on task similarity, leverage graph-based methods and apply distributed optimization techniques to implement FL systems. Detailed pseudocode, intuitive explanations, and implementation-ready algorithms ensure you not only understand the theory but can apply it in real-world systems.Topics such as privacy leakage analysis, model heterogeneity, and adversarial resilience are treated with both mathematical rigor and accessibility. Whether you're building decentralized AI for regulated industries or in settings where data, users, or system conditions change over time, this book equips you to design FL systems that are both performant and trustworthy.

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Taschenbuch. Zustand: Neu. Neuware - Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data. Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.