Federated Learning
Foundations and Applications
by Anwesha Mukherjee, Rajkumar Buyya, Sajal K. Das
1st Edition
Publisher: Morgan Kaufmann
Book Details
| Print ISBN | 9780443444333 |
| eText ISBN | 9780443444340 |
| Publisher | Morgan Kaufmann |
| Publishing Year | 2026 |
| Edition | 1st Edition |
| Language | English |
| Pages | 366 |
Federated Learning: Foundations and Applications, 1st Edition provides a technical reference on distributed computing paradigms for computer science researchers, artificial intelligence researchers, and software engineers. Authored by Anwesha Mukherjee, Rajkumar Buyya, and Sajal K. Das, the volume examines fundamental concepts across machine learning, deep learning, centralized training, and distributed learning processes.
The text presents detailed analyses of system architectures, algorithms, system models, and energy-efficiency techniques. Beyond basic distributed frameworks, the content incorporates advanced subjects such as quantum federated learning and blockchain-enabled federated learning systems.
To connect theoretical principles with operational environments, the work incorporates real-world case studies illustrating both centralized and decentralized federated learning deployments. These practical examples support academic researchers and software engineers in analyzing complex distributed learning systems.
Table of Contents
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
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