Author
Contributions by role
Author 1
Dhaya R
PNG University of Technology
Summary
Dr. Dhaya R is having more than 19 years of academic, research and administrative experience in Asia, Middle East, and Oceania. Currently she is working as Senior Faculty of Computer Engineering, School of Electrical and Communications Engineering, PNG University of Technology (Public University & Engineers Australia Accredited), Lae, Papua New Guinea. He received his Post-Doctoral Fellowship from University of Louisiana, USA, and Ph.D Degree from Manonmaniam sundaranar University, India. She has published more than 20 books, 150 articles in international/national journals/conferences and 04 patents. She is the series editor of a number of book series and serves in various editorial capacities of several international journals. Her research interests focus on Cloud computing, Artificial Intelligence, Embedded Systems, Machine -deep learning, Wireless Sensor Networks and Network Security. She received young engineer award by Institution of Engineers (IEI), Kolkata, India.
Edited Journals
ICCK Contributions

Open Access | Review Article | 27 June 2025
Federated Learning for Artificial Intelligence in Embedded Systems
ICCK Transactions on Emerging Topics in Artificial Intelligence | Volume 2, Issue 2: 91-115, 2025 | DOI: 10.62762/TETAI.2025.440076
Abstract
Federated Learning (FL) which eliminates the centralized data storage requirement by facilitating model training on diverse edge devices is now a promising paradigm for decentralized machine learning (ML). Applications involving privacy-preserving Artificial Intelligence (AI), including wearable technology, IoT networks, and smart healthcare appliances, can particularly benefit from this solution in embedded systems. By using on-device local data from devices such as sensors, embedded controllers, and smartphones, FL keeps confidential information local, minimizing the data transfer cost and privacy risks. Potentiality, challenges, and key applications of FL integration with embedded systems... More >

Graphical Abstract
Federated Learning for Artificial Intelligence in Embedded Systems