The effectiveness of federated learning in high-performance information systems and informatics-based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT-based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications.
Features:
Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users' privacy
Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy
Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area
Analyses the need for a personalized federated learning framework in cloud-edge and wireless-edge architecture for intelligent IoT applications
Comprises real-life case illustrations and examples to help consolidate understanding of topics presented in each chapter
This book is recommended for anyone interested in federated learning-based intelligent algorithms for smart communications.
Dr. Kaushal Kishor
He received his Ph.D. in Computer science and engineering from AKTU Lucknow, in the domain of Mobile Ad hoc Network. M.Tech & B.Tech in Computer Science & Engineering from UPTU Lucknow. Currently, he is working in ABES Institute of Technology, Ghaziabad as Professor Information Technology. He has supervised more than 50 projects for graduate and post graduate students. He has more than 19 years of experience of teaching. He is Gate Qualified 2003 score 94.5 percentile. He has book published and edited (1) "Cloud-based Intelligent Informative Engineering for Society 5.0" pp. 1-234. Chapman and Hall/CRC., 2023, eBook ISBN: 9781003213895 (2) Design and Analysis Algorithms (ISBN NO. 978-93-81695-20-3) (3) Computer Networks (ISBN NO. 978-93-81695-27-2) (4) Compiler Design (ISBN NO. 938169530 x and ISBN-13 9789381695302) (5) Design and Analysis of Algorithms: Techniques and Control Management (ISBN NO. 978-81-8220-516-1) (6) Computer Networks a System Approach (ISBN NO. 978-81-8220-516-3) (7) Compiler Design Principles, Techniques, and Tools (ISBN:978-81-8220-626-7) for various engineering field like B.Tech. and MCA student. He has published 40 papers in peer reviewed international/National journals and conferences. His research interest includes Artificial Intelligence, Computer Networks, Algorithm, Compiler Design Wireless and Sensor Networking.
ORCID Profile- https://orcid.org/0000-0002-7131-1389
Dr. PARMA NAND
He is PhD. in Computer Science & Engineering from IIT Roorkee, M.Tech & B.Tech in Computer Science & Engineering from IIT Delhi. Prof Parma Nand is having more than 29 years of experience both in industry and academia. He had received various awards like best teacher award from Union Minister, best students project guide award from Microsoft in 2015 and best faculty award from cognizant in 2016. He had successfully completed government funded projects and spearheaded last five IEEE International conferences on Computing, Communication & Automation (ICCCA), IEEE students chapters, Technovation Hackathon 2019, Technovation Hackathon 2020, International Conference on Computing, Communication, and Intelligent Systems (ICCCIS-2021). He is member Executive Council of IEEE UP section (R-10), member Executive Committee IEEE Computer and Signal Processing Society, member Exec. India council Computer Society, member Executive Council Computer Society of India, Noida section and has acted as an observer in many IEEE conferences. He is also having active memberships of ACM, IEEE, CSI, ACEEE, ISOC, IAENG, and IASCIT. He is life time member of Soft Computing Research Society (SCRS) and ISTE.
He has delivered many invited/key notes talks at International & National Conferences/ Workshops/Seminars in India & abroad. He has published more than 150 papers in peer reviewed international/national journals and conferences.
Dr. Vishal Jain
He is presently working as an Associate Professor at Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida, U. P., India. Before that, he has worked for several years as an Associate Professor at Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 16 years of experience in the academics. He obtained Ph.D (CSE), M.Tech (CSE), MBA (HR), MCA, MCP and CCNA. He has more than 1130 research citation indices with Google Scholar (h-index score 17 and i-10 index 27). He has authored more than 95 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 45 books with various reputed publishers, including Elsevier, Springer, DeGruyter, IET, River Publishers, Apple Academic Press, CRC, Taylor and Francis Group, Scrivener, Wiley, Emerald, NOVA Science, Bentham Books and IGI-Global. He is the series editor of 10 book series. He is life member of CSI, ISTE and senior member of IEEE. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks. He received a Young Active Member Award for the year 2012-13 from the Computer Society of India, Best Faculty Award for the year 2017 and Best Researcher Award for the year 2019 from BVICAM, New Delhi. ORCID ID: https://orcid.org/0000-0003-1126-7424.
Dr. Neetesh Saxena
He is currently an Associate Professor (lecturer) with the School of Computer Science and Informatics at Cardiff University, UK with more than 14 years of teaching/research experience in academia. Before joining CU, he was an Assistant Professor with Bournemouth University, UK. Prior to this, he was a Post-Doctoral Researcher in the School of Electrical and Computer Engineering at the Georgia Institute of Technology, USA. He was also with the Department of Computer Science, The State University of New York (SUNY) Korea, South Korea as a Post-Doctoral Researcher and a Visiting Scholar at the Department of Computer Science, Stony Brook University, USA. He earned my PhD in Computer Science and Engineering from Indian Institute of Technology (IIT), Indore, India. He was a DAAD Scholar at Bonn-Aachen International Center for Information Technology (B-IT), Rheinische- Friedrich-Wilhelms Universitaet, Bonn, Germany and was also a TCS Research Scholar. He is a Senior Member of IEEE and a member of IEEE SMC, IEEE ComSoc, ACM, and Eta Kappa Nu.
Dr. Gaurav Agarwal
Ph. D (CSE) IIT (ISM), Dhanbad | M. Tech (CSE) | B.E. (CSE)| NBA Expert | 2 International Awards | 1 National Award | 6+ SCI & 6+ SCOPUS | 4 National Patents (2 Granted) |123 Citations with h-index 5, Currently working as Associate Professor, Galgotias University, 20+ years of teaching experience with leading Engineering Institutions, Working for NBA Accreditation.
Dr. Rani Astya
She is serving the organization as a role of Assistant Professor in the Department of Computer Science & Engineering. Along with it, she is leading team-member of IGAP of this university She pursued her graduation (B.E.) in computer science Engg from Jiwaji University(Gwalior, MP) and post graduation(M.Tech) in Information Technology from IIT Roorkee Her research domains are OPTIMAL PATH PLANNING FOR MOBILE ROBOTS by Quadtree method.
Herausgeber*in
Abes Inst. of Tech.
Sharda University, India
Sharda University, Greater Noida, India
Cardiff University, UK
Galgotias University, India
1. Introduction to Federated Learning: Transforming Collaborative Machine Learning for a Decentralized Future 2. Applications, Challenges, and Opportunities for Federated Learning in 6G 3. Unleash Federated Machine Learning and Internet of Medical Things (IoMT) for Diseases Screening and Enhancement of Smart Healthcare 4. Federated Machine Learning in Medical Science: A Perspective Investigation 5. Artificial Intelligence Techniques Based on Federated Learning in Smart Healthcare 6. Federated Machine Learning in Medical Science: A Prospective Investigation 7. Healthcare Informatics Security Issues and Solutions using Federated Learning 8. Innovative Solutions: Exploring Federated Learning-Based Resource Virtualization with AR Integration in Healthcare Environments 9. Securing the Connected World: Federated Learning and IoT Cybersecurity 10. Federated Learning Shaping the Future of Smart City Infrastructure 11. EmPowering Teaching Institutes: Integrating Federated Learning in the Internet of Things (IOT) 12. A Critical Role for Federated Learning in IoT
