Foundations of Federated Learning

As data becomes increasingly distributed across devices, organizations, institutions, and jurisdictions, conventional centralized machine learning faces growing challenges related to privacy, regulation, communication costs, data ownership, and trust. This book provides a rigorous yet accessible introduction to federated learning as a privacy-aware, decentralized, and trustworthy approach to artificial intelligence. As data becomes increasingly distributed across devices, organizations, institutions, and jurisdictions, conventional centralized machine learning faces growing challenges related to privacy, regulation, communication costs, data ownership, and trust. The book explains the mathematical foundations, optimization methods, system architectures, and key algorithms that enable collaborative model training without centralizing sensitive data. It also examines heterogeneous data, scalability, fault tolerance, privacy and security mechanisms, governance, fairness, accountability, and regulatory compliance. Suitable for students, researchers, educators, and practitioners, it offers a unified framework for designing responsible, secure, and privacy-preserving AI systems worldwide.