連合学習の基礎（テキスト） Foundations of Federated Learning

著者名：Ray, Partha Pratim

Springer（2026/10/14発売）

• 言語：ENG
• ISBN：9783032340832
• eISBN：9783032340849

Description

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.

Table of Contents

Part I: Foundations of Federated Learning.- Chapter 1: Introduction to Federated Learning.- Chapter 2: Fundamentals and Definitions of Federated Learning.- Chapter 3: Mathematical Formulation of Federated Learning.- Part II: Federated Learning Architectures and Training Protocols.- Chapter 4: Federated Learning System Architectures.-Chapter 5: Decentralized and Peer-to-Peer Federated Learning.- Chapter 6: Federated Learning Training Process.- Part III: Data Heterogeneity and Non-IID Learning.- Chapter 7: Data Heterogeneity and Non-IID Challenges.- Chapter 8: Techniques for Handling Non-IID Data.- Part IV: Federated Optimization Algorithms.- Chapter 9: Foundations of Federated Optimization Algorithms.- Chapter 10: Federated Averaging.- Chapter 11: Advanced Federated Optimization
Methods.- Part V: Personalized, Heterogeneous, and Hybrid Federated Learning.- Chapter 12: Personalized Federated Learning.- Chapter 13: Heterogeneous Federated Learning.- Chapter 14: Hybrid Federated Learning.- Part VI: Privacy, Security, Governance, and Applications.- Chapter 15: Privacy-Preserving Techniques in Federated Learning.- Chapter 16: Security Threats and Robust Federated Learning.- Chapter 17: Governance, Ethics, and Incentive Mechanisms.- Chapter 18: Applications, Emerging Trends and Tools.