Federated Learning

Foundations and Applications
Erscheinungsjahr 2026
ISBN: 978-0-443-44433-3
Verlag: Elsevier Science

Buch, Englisch, 250 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Sections cover fundamental concepts, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to coverage of 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.

Federated Learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchange of only model parameters between clients and servers, hence the addition of this new release is ideal for those interested in the topics presented.

Autoren/Hrsg.

Mukherjee, Anwesha

Das, Sajal K

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme

Inhaltsverzeichnis

1. An Introduction to Federated Learning
2. Centralized versus Decentralized Federated Learning
3. Optimization Techniques for Federated Learning Algorithms
4. Federated Learning Framework with Battery-Aware Clients
5. Rethinking SDN Security: From Centralized Learning to Privacy-Enhanced DDoS Detection with Federated Learning and Differential Privacy
6. Secure Federated Learning with Hindmarsh-Rose encryption
7. Investigating the Resilience of Federated Learning: Perspectives on Attacks and Defenses
8. Advancing Privacy and Robustness in Federated Learning: Strategies for Robust Defense Against Inference Attacks and Differential Privacy Integration in Federated Learning
9. Federated Learning Framework for Survival Analysis in Healthcare
10. Vertical Federated Learning with Feature and Sample Privacy
11. Quantum Computing-based Federated Learning

Über Autor(innen)

Mukherjee, Anwesha
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.

Das, Sajal K
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).