Summary

Dr. Shahnila Rahim is an Assistant Professor at Noroff University College and an active researcher in artificial intelligence (AI) and wireless communications. Her work focuses on applying AI techniques to optimize UAV operations, enhance 5G and 6G network performance, and model wireless channels. She also explores AI-driven solutions for Space-Air-Ground Networks and non-terrestrial networks. Her recent research includes developing adaptive algorithms for energy-efficient data transmission and integrating temporal graph models with AI for dynamic network optimization. She is passionate about advancing intelligent, automated wireless systems through interdisciplinary AI approaches. In addition to research, Dr. Rahim has taught courses like data structures, object-oriented programming, programming fundamentals, and operating systems, and actively mentors master's students in advanced AI and network modeling projects.

Edited Journals

ICCK Contributions

Articles 1 Edited 0
Open Access | Editorial | 09 December 2025
Navigating Ethical Boundaries in Federated Learning for Biomedical Research
by
Shahnila Rahim
,
Xiao Kong
,
Fatima Abdullah
Journal of Artificial Intelligence in Bioinformatics | Volume 1, Issue 2: 72-78, 2025 | DOI: 10.62762/JAIB.2025.703433
Abstract
Biomedical research is increasingly shaped by vast and diverse datasets, yet their integration is constrained by privacy concerns, regulatory barriers, and fragmented infrastructures. Federated learning (FL) has emerged as a promising paradigm that enables institutions to collaboratively train machine learning models while keeping sensitive data local. This approach has the potential to accelerate discovery in areas such as precision medicine, rare disease research, and population health by pooling knowledge without centralizing data. However, federated learning also introduces new ethical and governance challenges. Risks of information leakage, inequitable participation, algorithmic bias, u... More >
Graphical Abstract
Navigating Ethical Boundaries in Federated Learning for Biomedical Research