Federated Learning

Foundations and Applications

Rajkumar Buyya, Anwesha Mukherjee, Sajal K Das

• Publisher: Morgan Kaufmann
• Year: 2026
• Topic: Computer Science
• eBook ISBN:
  9780443444340
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Federated learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchanging only model parameters between clients and servers. This book covers the fundamental concepts of federated learning, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to cover the architectures, algorithms, and system models of federated learning, as well as security, privacy, and energy-efficiency techniques. Finally, the book presents various applications of federated learning through real-world case studies illustrating both centralized and decentralized federated learning. - Presents detailed discussion of the architectures, algorithms, and applications of federated learning - Covers advanced optimization techniques for federated learning algorithms to improve the efficiency and effectiveness of decentralized learning systems - Strikes a balance between the ideas presented, frequently bridging new and engaging material to the fundamental chemistry principle - Shares high-level federated learning security architectures such as FedBoxGuard, which targets single-controller SDN setups by placing "white boxes" between the data and control planes, and FedLiV, which tackles the non-IID data problem by using heterogeneous models - Presents advanced techniques such as differential privacy, Poisson binomial mechanism vertical federated learning (PBM-VFL), a communication-efficient vertical federated learning algorithm, quantum federated learning, and blockchain-enabled federated learning

Table of contents

• Title of Book
• Chapter 1: Federated learning at a glance
• Chapter 2: Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications
• Chapter 3: Centralized versus decentralized federated learning
• Chapter 4: Optimization techniques for federated learning algorithms
• Chapter 5: Federated learning framework with battery-aware clients
• Chapter 6: Bridging data privacy and intelligence: the landscape of federated learning
• Chapter 7: Vertical federated learning with feature and sample privacy
• Chapter 8: Privacy-enhanced DDoS detection with federated learning and differential privacy
• Chapter 9: Secure federated learning with Hindmarsh-Rose encryption
• Chapter 10: Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy
• Chapter 11: Resilience of federated learning: perspectives on attacks and defenses
• Chapter 12: Robust defense against inference attacks and differential privacy integration in federated learning
• Chapter 13: Blockchain-enabled federated learning
• Chapter 14: Incentive-based federated learning: architectural elements and future directions
• Chapter 15: Adaptive training and aggregation for federated learning in multi-tier computing networks
• Chapter 16: Privacy-preserving federated learning in IoT for smart and sustainable healthcare
• Chapter 17: Federated learning framework for survival analysis in healthcare
• Chapter 18: Federated learning applications in 6G communications and smart societies
• Chapter 19: Quantum federated learning: architectural elements and future directions
• Index