Federated Learning
Foundations and Applications
Paperback Engels 2026 9780443444333Samenvatting
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.
Specificaties
Lezersrecensies
Inhoudsopgave
2. Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications
3. Centralized versus decentralized federated learning
4. Optimization techniques for federated learning algorithms
5. Federated learning framework with battery-aware clients
6. Bridging data privacy and intelligence: the landscape of federated learning
7. Vertical federated learning with feature and sample privacy
8. Privacy-enhanced DDoS detection with federated learning and differential privacy
9. Secure federated learning with Hindmarsh-Rose encryption
10. Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy
11. Resilience of federated learning: perspectives on attacks and defenses
12. Robust defense against inference attacks and differential privacy integration in federated learning
13. Blockchain-enabled federated learning
14. Incentive-based federated learning: architectural elements and future directions
15. Adaptive training and aggregation for federated learning in multi-tier computing networks
16. Privacy-preserving federated learning in IoT for smart and sustainable healthcare
17. Federated learning framework for survival analysis in healthcare
18. Federated learning applications in 6G communications and smart societies
19. Quantum federated learning: architectural elements and future directions
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