Federated Learning

Foundations and Applications

Häftad, Engelska, 2026

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Beskrivning

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

Produktinformation

Utgivningsdatum:2026-05-27
Mått:216 x 276 x 18 mm
Vikt:990 g
Format:Häftad
Språk:Engelska
Antal sidor:366
Förlag:Elsevier Science
ISBN:9780443444333

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

Dr. Rajkumar Buyya is Redmond Barry Distinguished Professor and Director of the Cloud Computing andDistributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as thefounding CEO of Manjrasoft, a spin-off company of the University, commercializing its innovations in CloudComputing. He has authored over 650 publications and seven textbooks including Mastering Cloud Computingfrom McGraw Hill, China Machine Press, and Morgan Kaufmann for Indian, Chinese and international marketsrespectively. Dr. Buyya is one of the most highly-cited authors in Computer Sience and Software Engineeringworldwide. “A Scientometric Analysis of Cloud Computing Literature by German scientists ranked Dr. Buyyaas the World's Top-Cited Author and the World's Most-Productive Author in Cloud Computing. He has beenrecognized as a Web of Science “Highly Cited Researcher for four consecutive years since 2016. Dr. Buyyawas recognized as Scopus Researcher of the Year 2017 with Excellence in Innovative Research Award fromElsevier; "Lifetime Achievement Awards" from two Indian universities, and the “Best of the World, in theComputing Systems field, by The Australian 2019 Research Review. Software technologies for Grid, Cloud, andFog computing developed under Dr. Buyya's leadership have gained rapid acceptance and are in use at severalacademic institutions and commercial enterprises in 40 countries around the world. Dr. Buyya has led theestablishment and development of key community activities, including serving as foundation Chair of the IEEETechnical Committee on Scalable Computing and five IEEE/ACM conferences. These contributions and theinternational research leadership of Dr. Buyya are recognized through the award of the “2009 IEEE Medal forExcellence in Scalable Computing from the IEEE Computer Society TCSC. Manjrasoft's Aneka Cloudtechnology 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 currentlyserving as Editor-in-Chief of Software: Practice and Experience, a long-standing journal in the field, establishedmore than 50 years ago. 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. 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).

Innehållsförteckning

1. Federated learning at a glance2. Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications3. Centralized versus decentralized federated learning4. Optimization techniques for federated learning algorithms5. Federated learning framework with battery-aware clients6. Bridging data privacy and intelligence: the landscape of federated learning7. Vertical federated learning with feature and sample privacy8. Privacy-enhanced DDoS detection with federated learning and differential privacy9. Secure federated learning with Hindmarsh-Rose encryption10. Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy11. Resilience of federated learning: perspectives on attacks and defenses12. Robust defense against inference attacks and differential privacy integration in federated learning13. Blockchain-enabled federated learning14. Incentive-based federated learning: architectural elements and future directions15. Adaptive training and aggregation for federated learning in multi-tier computing networks16. Privacy-preserving federated learning in IoT for smart and sustainable healthcare17. Federated learning framework for survival analysis in healthcare18. Federated learning applications in 6G communications and smart societies19. Quantum federated learning: architectural elements and future directions