Anglický jazyk

Federated Learning

Vydavateľstvo: Springer-Verlag GmbH
Rok vydania: 2026
Formát: Hardback
Rozmer: 235 x 155 mm
Jazyk: Anglický jazyk
ISBN: 9789819510085

Predpokladaný dátum vydania: 15.1.2026

64.34 €

bežná cena: 71.49 €

O knihe

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 volume 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 to drive model training, and apply optimization tools like generalized total variation minimization (GTVMin) for better results. 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 secure aggregation, privacy leakage analysis, model heterogeneity, and adversarial resilience are treated with both mathematical rigor and accessibility. Whether you’re developing decentralized AI for regulated industries or studying machine learning in dynamic environments, this book equips you to design FL systems that are not only effective but also ethical.