Description
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
1. Federated learning at a glance - Anwesha Mukherjee, Sajal K. Das, and Rajkumar Buyya2. Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications - Fatemeh Mirhakimi, Nan Yang, Rodrigo N. Calheiros, Bahman Javadi, and Feng Yan3. Centralized versus decentralized federated learning - Irina Arévalo and Jose L. Salmeron4. Optimization techniques for federated learning algorithms - Ferdinand Kahenga, Antoine Bagula, Sajal K. Das, Jovita Mateus, and Olasupo Ajayi5. Federated learning framework with battery-aware clients - Andrea Augello, Priyesh Ranjan, Ashish Gupta, Federico Corò, Giuseppe Lo Re, and Sajal K. Das6. Bridging data privacy and intelligence: the landscape of federated learning - Dipanwita Thakur and Sajal K. Das7. Vertical federated learning with feature and sample privacy - Linh Tran, Timothy Castiglia, Stacy Patterson, and Ana Milanova8. Privacy-enhanced DDoS detection with federated learning and differential privacy - Jovita Mateus, Antoine Bagula, Guy-Alain Lusilao Zodi, Olasupo Ajayi, and Ferdinand Kahenga9. Secure federated learning with Hindmarsh-Rose encryption - Jose L. Salmeron and Irina Arévalo10. Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy - Turki Alhazmi and Farag Azzedin11. Resilience of federated learning: perspectives on attacks and defenses - Pravija Raj P V, Ashish Gupta, and Sajal K. Das12. Robust defense against inference attacks and differential privacy integration in federated learning - M.A.P. Chamikara and Mohan Baruwal Chhetri13. Blockchain-enabled federated learning - Murtaza Rangwala, K.R. Venugopal, and Rajkumar Buyya14. Incentive-based federated learning: architectural elements and future directions - Chanuka A.S. Hewa Kaluannakkage and Rajkumar Buyya15. 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 Tian16. Privacy-preserving federated learning in IoT for smart and sustainable healthcare - Shinu M. Rajagopal, Supriya M, and Rajkumar Buyya17. Federated learning framework for survival analysis in healthcare - Navid Seidi, Satyaki Roy, and Sajal K. Das18. Federated learning applications in 6G communications and smart societies - Radical Rakhman Wahid and Farag Azzedin19. Quantum federated learning: architectural elements and future directions - Siva Sai, Abhishek Sawaika, Prabhjot Singh, and Rajkumar BuyyaIndex
-
- 電子書籍
- 【デジタル限定】木村絵里子写真集「見た…
