Federated Learning 1st Edition

Federated Learning, 1st Edition by Lam M. Nguyen, Trong Nghia Hoang, and Pin-Yu Chen is a contemporary guide that brings together theory, algorithms, and real-world perspectives on training machine learning models without centralizing data. This authoritative volume captures the momentum behind privacy-preserving, distributed learning and explains how organizations can leverage decentralized data from edge devices, mobile phones, and enterprise silos.

Start with a clear, engaging overview of core concepts—federated optimization, secure aggregation, communication-efficient updates, and robustness to heterogeneity—followed by progressively deeper treatments of algorithmic design and theoretical guarantees. Written for researchers, data scientists, and engineers, the book balances rigorous math with intuitive explanations and practical insights into deployment challenges such as communication constraints, system architectures, and privacy-compliance considerations.

Why this title matters: practitioners building AI systems in healthcare, finance, IoT, and mobile services will find actionable strategies to reduce data movement while maintaining model accuracy and regulatory compliance. Academics will appreciate the thorough proofs and references that position this edition as a reliable resource for courses and research. The global relevance is clear—tech teams across North America, Europe, and the Asia-Pacific region will benefit from its practical guidance and forward-looking case studies.

Whether you’re architecting federated systems or seeking a deep conceptual foundation, Federated Learning, 1st Edition equips you with the knowledge to design scalable, secure, and effective distributed learning solutions. Secure your copy today and lead the next wave of privacy-first AI innovation.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.