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Elsevier

Federated Learning

Foundations and Applications

  • 1 Edición - 19 de mayo de 2026
  • Última edición
  • Editores: Rajkumar Buyya, Anwesha Mukherjee, Sajal K Das
  • Idioma: Inglés

Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learni… Leer más

Descripción

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.

Puntos claves

  • 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

De interès para

Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts and software engineers

Índice

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

Index

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 27 de mayo de 2026
  • Idioma: Inglés

Sobre los editores

RB

Rajkumar Buyya

Dr. Rajkumar Buyya is Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the founding CEO of Manjrasoft, a spin-off company of the University, commercializing its innovations in Cloud Computing. He has authored over 650 publications and seven textbooks including Mastering Cloud Computing from McGraw Hill, China Machine Press, and Morgan Kaufmann for Indian, Chinese and international markets respectively. Dr. Buyya is one of the most highly-cited authors in Computer Sience and Software Engineering worldwide. “A Scientometric Analysis of Cloud Computing Literature” by German scientists ranked Dr. Buyya as the World's Top-Cited Author and the World's Most-Productive Author in Cloud Computing. He has been recognized as a Web of Science “Highly Cited Researcher” for four consecutive years since 2016. Dr. Buyya was recognized as Scopus Researcher of the Year 2017 with Excellence in Innovative Research Award from Elsevier; "Lifetime Achievement Awards" from two Indian universities, and the “Best of the World,” in the Computing Systems field, by The Australian 2019 Research Review. Software technologies for Grid, Cloud, and Fog computing developed under Dr. Buyya's leadership have gained rapid acceptance and are in use at several academic institutions and commercial enterprises in 40 countries around the world. Dr. Buyya has led the establishment and development of key community activities, including serving as foundation Chair of the IEEE Technical Committee on Scalable Computing and five IEEE/ACM conferences. These contributions and the international research leadership of Dr. Buyya are recognized through the award of the “2009 IEEE Medal for Excellence in Scalable Computing” from the IEEE Computer Society TCSC. Manjrasoft's Aneka Cloud technology developed under his leadership has received the "Frost & Sullivan New Product Innovation Award." Dr. Buyya served as founding Editor-in-Chief of the IEEE Transactions on Cloud Computing. He is currently serving as Editor-in-Chief of Software: Practice and Experience, a long-standing journal in the field, established more than 50 years ago.
Afiliaciones y experiencia
University of Melbourne, Australia

AM

Anwesha Mukherjee

Dr. Anwesha Mukherjee has received B. Tech in Information Technology from Kalyani Govt. Engineering College in 2009. She has received M. Tech in Information Technology from West Bengal University of Technology in 2011. She stood first class first in M. Tech and received Inspire Fellowship from the Department of Science & Technology, Govt. of India to pursue her Ph.D. She has received Ph.D. in Computer Science and Engineering from West Bengal University of Technology in 2018. She has worked as a Research Associate in the computer science department of IIT Kharagpur. She is currently working as an Assistant Professor and Head of the Department of Computer Science, Mahishadal Raj College, West Bengal, India. She is Research Visitor in the CLOUD Lab, The University of Melbourne. Her research areas include IoT, Fog computing, mobile network, Geospatial informatics and mobile cloud computing. She has received Young Scientist Award from International Union of Radio Science in 2014, 2020, and 2021. She has more than eighty research publications in international journals, conference proceedings, book chapters, and three edited books.

Afiliaciones y experiencia
Department of Computer Science, Mahishadal Raj College (Vidyasagar University), Mahishadal, India

SD

Sajal K Das

Dr. Sajal K. Das is the Curators’ Distinguished Professor and Daniel St. Clair Endowed Chair in Computer Science at Missouri University of Science and Technology, where he was the Chair of Computer Science Department during 2013-2017. He also served the US National Science Foundation (NSF) as a Program Director in the Computer and Network Systems Division. Dr. Das’ interdisciplinary research spans cyber-physical systems, IoT, cybersecurity, machine learning, data science, wireless and sensor networks, mobile and pervasive computing, smart environments, parallel/cloud/edge computing, social and biological networks, applied graph theory and game theory. He has contributed significantly to these areas and published extensively in top-tier venues (more than 350 journal articles and more than 450 peer-reviewed conference papers). He coauthored four books, 59 book chapters, and 5 US patents. He directed over $24 million funded research projects. His h-index is 99 with more than 42,000 citations.

Dr. Das is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal and serves as an Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Sustainable Computing, IEEE/ACM transactions on Networking, ACM Transactions on Sensor Networks, and Journal of Parallel and Distributed Computing. A founder of the IEEE PerCom, WoWMoM, SMARTCOMP and ACM ICDCN conferences, he has served as General and Program Chair of reputed conferences. He is a recipient of 12 Best Paper Awards in flagship conferences like ACM MobiCom and IEEE PerCom; and numerous awards for teaching, mentoring and research including the IEEE Computer Society’s Technical Achievement award for pioneering contributions to sensor networks and mobile computing, and the University of Missouri System President’s Award for Sustained Career Excellence. Dr. Das has mentored and graduated 12 postdoctoral fellows, 51 Ph.D. scholars, 31 MS thesis, and numerous undergraduate research students. Currently he is supervising 9 Ph.D. students and 4 postdocs. He is a Distinguished alumnus of the Indian Institute of Science, Bangalore and a Fellow of the IEEE, National Academy of Inventors (NAI) and Asia-Pacific Artificial Intelligence Association (AAIA).

Afiliaciones y experiencia
Curators' Distinguished Professor & Daniel St. Clair Endowed Chair, Computer Science, Missouri University of Science and Technology, Rolla, MO, USA