Foundations of Federated Learning

Beschreibung

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.

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.

Autor*in

Partha Pratim Ray

Themen in »Foundations of Federated Learning«

Mathematical Foundations of Federated Learning Privacy Preserving Machine Learning Systems Decentralized Artificial Intelligence Models Federated Optimization Algorithms and Theory Non-IID Data in Federated Learning Federated Averaging and FedProx Algorithms Personalized Federated Learning Methods Secure Aggregation in Federated Learning Differential Privacy for Federated Learning Robust Federated Learning Against Attacks Byzantine Failures in Federated Systems Federated Learning for Trustworthy AI Governance and Ethics in Federated Learning Federated Learning System Architectures Peer-to-Peer Federated Learning Models

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