Buch, Englisch, 366 Seiten, Format (B × H): 216 mm x 276 mm, Gewicht: 449 g
Foundations and Applications
Buch, Englisch, 366 Seiten, Format (B × H): 216 mm x 276 mm, Gewicht: 449 g
ISBN: 978-0-443-44433-3
Verlag: Elsevier 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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Federated learning at a glance - Anwesha Mukherjee, Sajal K. Das, and Rajkumar Buyya
2. Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications - Fatemeh Mirhakimi, Nan Yang, Rodrigo N. Calheiros, Bahman Javadi, and Feng Yan
3. Centralized versus decentralized federated learning - Irina Arévalo and Jose L. Salmeron
4. Optimization techniques for federated learning algorithms - Ferdinand Kahenga, Antoine Bagula, Sajal K. Das, Jovita Mateus, and Olasupo Ajayi
5. Federated learning framework with battery-aware clients - Andrea Augello, Priyesh Ranjan, Ashish Gupta, Federico Corò, Giuseppe Lo Re, and Sajal K. Das
6. Bridging data privacy and intelligence: the landscape of federated learning - Dipanwita Thakur and Sajal K. Das
7. Vertical federated learning with feature and sample privacy - Linh Tran, Timothy Castiglia, Stacy Patterson, and Ana Milanova
8. Privacy-enhanced DDoS detection with federated learning and differential privacy - Jovita Mateus, Antoine Bagula, Guy-Alain Lusilao Zodi, Olasupo Ajayi, and Ferdinand Kahenga
9. Secure federated learning with Hindmarsh-Rose encryption - Jose L. Salmeron and Irina Arévalo
10. Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy - Turki Alhazmi and Farag Azzedin
11. Resilience of federated learning: perspectives on attacks and defenses - Pravija Raj P V, Ashish Gupta, and Sajal K. Das
12. Robust defense against inference attacks and differential privacy integration in federated learning - M.A.P. Chamikara and Mohan Baruwal Chhetri
13. Blockchain-enabled federated learning - Murtaza Rangwala, K.R. Venugopal, and Rajkumar Buyya
14. Incentive-based federated learning: architectural elements and future directions - Chanuka A.S. Hewa Kaluannakkage and Rajkumar Buyya
15. Adaptive training and aggregation for federated learning in multi-tier computing networks - Wenjing Hou, Hong Wen, Ning Zhang, Wenxin Lei, Haojie Lin, Zhu Han, Qiang Liu, and Wenhong Tian
16. Privacy-preserving federated learning in IoT for smart and sustainable healthcare - Shinu M. Rajagopal, Supriya M, and Rajkumar Buyya
17. Federated learning framework for survival analysis in healthcare - Navid Seidi, Satyaki Roy, and Sajal K. Das
18. Federated learning applications in 6G communications and smart societies - Radical Rakhman Wahid and Farag Azzedin
19. Quantum federated learning: architectural elements and future directions - Siva Sai, Abhishek Sawaika, Prabhjot Singh, and Rajkumar Buyya
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