[LGPR] Find a Funder Publication Success in Scientific Reports- Dr Venkadeshan Ramalingam
- Scientific Progress
- Jan 24
- 2 min read
We are delighted to share yet another success story from our newly launched Find a Funder initiative. Through the collaborative efforts of LUCM, CREP, and Vibrasphere Technologies, we have successfully secured open access fee funding of USD 36,000 per candidate under the LGPR Programme.
Dr Venkadeshan Ramalingam, LGPR candidate has received full open access fee support worth 2590 USD for his publication in the prestigious Scientific Reports Journal.
Scientific Reports, 16, 3071 (2026)
Journal Indexes: Impact factor 3.9
Indexing: SCI, SCIE & EI Compendex
H-Index: 347
Aims & Scope
Scientific Reports is an open access journal publishing original research from across all areas of the natural sciences, psychology, medicine and engineering.
A hybrid federated learning framework with generative AI for privacy-preserving and sustainable security in IOT-enabled smart environments
Venkadeshan Ramalingam*
Lincoln University College, Petaling Jaya, Malaysia.
Basant Kumar
Modern College of Business & Science, Muscat, Oman
Shashi Kant Gupta
Lincoln University College-KL Malaysia.
Deema Mohammed Alsekait
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
Diaa Salama AbdElminaam
Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
Jadara Research Centre, Jadara University, Irbid, Jordan
Abstract
The dramatic increase in IoT devices in a smart ecosystem like smart cities, transportation systems, and healthcare and industrial automation has greatly improved network connectivity and data-driven informed decisions. But this extraordinary level of connectivity generates important concerns associated with sensitive information and security risks. Therefore, this study proposes a novel framework for secure and sustainable IoT network and devices through a combination of a Hybrid Federated Learning Framework and GenAI. The proposed framework focuses on extending a secure learning platform for all different IoT devices through a Federated Learning Framework and utilizing GenAI capabilities for advanced information augmentation and customized anomaly detection. To improve the level of guaranteed privacy, this framework will utilize differential privacy techniques and a blockchain-assisted model validation process. Moreover, techniques for energy-efficient model optimization and edge intelligence in making decisions are considered to improve sustainability. The proposed work will examine and develop this novel hybrid model through intensive simulations and lab-based testing for its application in a building and energy management field. The impact will include a new federative generative architecture that offers enhanced cyber threat resilience, lower overhead costs of communication, and ensures user confidentiality of data. The end goal of this proposed project is to contribute positively towards advancing the state-of-the-art in sustainable AI for a secure and environment-conscious IoT.
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