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Federated Learning for IoT Applications

Federated Learning for IoT Applications
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Satya Prakash Yadav
Springer International Publishing
 Paperback
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Artikel-Nr:
9783030855611
Veröffentl:
2023
Einband:
Paperback
Erscheinungsdatum:
04.02.2023
Seiten:
276
Autor:
Satya Prakash Yadav
Gewicht:
423 g
Format:
235x155x16 mm
Serie:
EAI/Springer Innovations in Communication and Computing
Sprache:
Englisch
Beschreibung:

Dr. Satya Prakash Yadav is currently the Associate Professor of the Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM), Greater Noida (India). He has awarded his PhD degree entitled "Fusion of Medical Images in Wavelet Domain" to Dr. A.P.J. Abdul Kalam Technical University (AKTU) (formerly UPTU).  A seasoned academician having more than 14 years of experience, he has published four books (Programming in C, Programming in C++ and Blockchain and Cryptocurrency) under I.K. International Publishing House Pvt. Ltd. Including Distributed Artificial Intelligence: A Modern Approach, Published December 18, 2020 by CRC Press. He has undergone industrial training programs during which he was involved in live projects with companies in the areas of SAP, Railway Traffic Management Systems, and Visual Vehicles Counter and Classification (used in the Metro rail network design). He is an alumnus of Netaji Subhas Institute of Technology (NSIT), Delhi University. A prolific writer, Dr. Satya Prakash Yadav has published two patents and authored many research papers in web of science indexed journals. Additionally, he has presented research papers at many conferences in the areas of Image Processing, Information retrieval, Features extraction  and Programming, such Digital Image Processing, Feature Extraction, Information Retrieval, C, Data Structure, C++, C# and Java. Also, he is a lead editor in CRC Press, Taylor and Francis Group Publisher (U.S.A), Tech Science Press (Computer Systems Science and Engineering), International Springer Publisher, Science Publishing Group,(U.S.A), and Eureka Journals , Pune ( India).

Dr. Bhoopesh Singh Bhati is presently working as an Associate Professor in Chandigarh University, Mohali. He received his Ph. D (Computer Science and Engineering) from the University School of Information Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi. He has obtained his M. Tech. (Information Security) and B. Tech. (Computer Science and Engineering) from  Guru Gobind Singh Indraprastha University, Delhi, in 2009 and 2012 respectively. Dr. Bhati has worked as an Assistant Professor in Ambedkar Institute of Advanced Communication Technologies & Research Govt. of N.C.T Delhi, Geeta colony, Delhi, India.  He has published various research papers in highly reputed, SSCI/SCI/SCIE- Indexed Journals including Elsevier, Wiley, Springer, Inderscience, etc. Dr. Bhati is a recognized/ Ad-hoc reviewer of various reputed journals of Elsevier, Wiley, Springer, etc. Dr. Bhati has also participated and presented paper in Springer International Conference (RICE 2019) held in Vietnam. His current research area Intrusion Detection, Operating System, Data Science and IoT.

Dr. Dharmendra Prasad Mahato is currently an assistant professor in the Department of Computer Science and Engineering at National Institute of Technology Hamirpur, Himachal Pradesh, India. He received his AMIETE degree in Computer Science and Engineering with distinction from the Institute of Electronics and Telecommunication Engineers (IETE), India, in 2011. He received his Master of Technology in Computer Science and Engineering from Atal Bihari Vajpayee-Indian Institute of Information Technology and Management Gwalior in 2013 and Ph.D. in Computer Science and Engineering from Indian Institute of Technology (Banaras Hindu University), Varanasi, India, in January 2018. His research interests include distributed computing, artificial intelligence, operating systems, databases and modeling and simulation. He has published in journals such as Applied Soft Computing, Swarm and Evolutionary Computation, ISA Transactions, Cluster Computing, Concurrency and Computation: Practice and Experience, and conferences such as AINA, ICPP, ICDCN, and E-Science.

Dr. Sachin Kumar received the B.Tech. degree from Uttar Pradesh Technical University, Lucknow, I

This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users' privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federatedlearning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.
Shows how federated learning utilizes data generated by consumer devices without intruding privacy, allowing machine learning models to deliver personalized services
Chapter 1. Introduction to Federated Learning.- Chapter 2. Federated Learning for IoT Devices.- Chapter 3. Personalized Federated Learning.- Chapter 4. Federated Learning for an IoT Application.- Chapter 5. Some observations on the behaviour of Federated Learning.- Chapter 6. Federated Learning with Cooperating Devices: A Consensus Approach.- Chapter 7. A prospective study of federated machine learning in medical image fusion.- Chapter 8. Communication-Efficient Federated Learning in Wireless-Edge Architecture.- Chapter 9. Towards Ubiquitous AI in 6G with Federated Learning.- Chapter 10. Federated Learning using Tensor Flow.- Chapter 11. Cyber Security and privacy of Connected and Automated Vehicles (CAVs) based Federated Learning:  Challenges, Opportunities and Open Issues.- Chapter 12. Security Issues & Solutions for Healthcare Informatics.- Chapter 13. Federated Learning: Challenges, Methods, and Future Directions.- Chapter 14. Quantum Federated Learning for Wireless Communications.- Chapter 15. Federated machine learning with data mining in health care.- Chapter 16.  Federated Learning for data mining in Healthcare.

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