Federated Learning for IoT Applications

lt;p>Satya Prakash Yadav is currently the faculty of the Information Technology Department, ABES Institute of Technology (ABESIT), Ghaziabad (India). He has awarded his Ph.D. 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 13 years of experience, he has published three books (Programming in C, Programming in C++ and Blockchain and Cryptocurrency) under I.K. International Publishing House Pvt. Ltd. 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, Feature Extracction and Programming, such C, Data Structure, C++, C# and Java. Also, he is a lead editor in Science Publishing Group,(U.S.A), and Eureka Journals , Pune ( India).

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.

Sachin Kumar received the B.Tech. degree from Uttar Pradesh Technical University, Lucknow, India, in 2009, and the M.Tech. and Ph.D. degrees from Guru Gobind Singh Indraprastha University, Delhi, India, in 2011 and 2016, respectively. He is currently a Research Professor with the College of IT Engineering, Kyungpook National University, Daegu, South Korea. He has published two patents and over a hundred research articles in several peer-reviewed international journals and conferences. He serves as the session chair, organizer, and member of the program committee for various conferences, workshops, and short

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.