Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Sections cover fundamental concepts, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to coverage of 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.
Federated Learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchange of only model parameters between clients and servers, hence the addition of this new release is ideal for those interested in the topics presented.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. Chapter 1: Federated learning at a glance - p. 1 8. Chapter 2: Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications - p. 11 9. Chapter 3: Centralized versus decentralized federated learning - p. 27 10. Chapter 4: Optimization techniques for federated learning algorithms - p. 43 11. Chapter 5: Federated learning framework with battery-aware clients - p. 63 12. Chapter 6: Bridging data privacy and intelligence: the landscape of federated learning - p. 83 13. Chapter 7: Vertical federated learning with feature and sample privacy - p. 107 14. Chapter 8: Privacy-enhanced DDoS detection with federated learning and differential privacy - p. 125 15. Chapter 9: Secure federated learning with Hindmarsh-Rose encryption - p. 145 16. Chapter 10: Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy - p. 151 17. Chapter 11: Resilience of federated learning: perspectives on attacks and defenses - p. 171 18. Chapter 12: Robust defense against inference attacks and differential privacy integration in federated learning - p. 195 19. Chapter 13: Blockchain-enabled federated learning - p. 219 20. Chapter 14: Incentive-based federated learning: architectural elements and future directions - p. 241 21. Chapter 15: Adaptive training and aggregation for federated learning in multi-tier computing networks - p. 259 22. Chapter 16: Privacy-preserving federated learning in IoT for smart and sustainable healthcare - p. 275 23. Chapter 17: Federated learning framework for survival analysis in healthcare - p. 285 24. Chapter 18: Federated learning applications in 6G communications and smart societies - p. 303 25. Chapter 19: Quantum federated learning: architectural elements and future directions - p. 325 26. Index - p. 345
Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Sections cover fundamental concepts, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to coverage of 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.
Federated Learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchange of only model parameters between clients and servers, hence the addition of this new release is ideal for those interested in the topics presented.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. Chapter 1: Federated learning at a glance - p. 1 8. Chapter 2: Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications - p. 11 9. Chapter 3: Centralized versus decentralized federated learning - p. 27 10. Chapter 4: Optimization techniques for federated learning algorithms - p. 43 11. Chapter 5: Federated learning framework with battery-aware clients - p. 63 12. Chapter 6: Bridging data privacy and intelligence: the landscape of federated learning - p. 83 13. Chapter 7: Vertical federated learning with feature and sample privacy - p. 107 14. Chapter 8: Privacy-enhanced DDoS detection with federated learning and differential privacy - p. 125 15. Chapter 9: Secure federated learning with Hindmarsh-Rose encryption - p. 145 16. Chapter 10: Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy - p. 151 17. Chapter 11: Resilience of federated learning: perspectives on attacks and defenses - p. 171 18. Chapter 12: Robust defense against inference attacks and differential privacy integration in federated learning - p. 195 19. Chapter 13: Blockchain-enabled federated learning - p. 219 20. Chapter 14: Incentive-based federated learning: architectural elements and future directions - p. 241 21. Chapter 15: Adaptive training and aggregation for federated learning in multi-tier computing networks - p. 259 22. Chapter 16: Privacy-preserving federated learning in IoT for smart and sustainable healthcare - p. 275 23. Chapter 17: Federated learning framework for survival analysis in healthcare - p. 285 24. Chapter 18: Federated learning applications in 6G communications and smart societies - p. 303 25. Chapter 19: Quantum federated learning: architectural elements and future directions - p. 325 26. Index - p. 345
