Personal profile
Biography
Dr Rehman is serving as Senior Lecturer in Computer Science at the Faculty of Creative Arts, Technologies & Science, University of Bedfordshire. His research interests revolve around the problem of federated learning in hospital environments and, more precisely, medical imaging AI. He is also exploring differential privacy, homomorphic encryption, and distributed governance models for federated learning systems. Much of his previous work was focused on the integration of blockchain technologies in different application areas such as cryptocurrencies, IoT devices, fog computing, 5G, and distributed oracles.
He had introduced dynamic and adaptive device-centric application execution models in edge-cloud-enabled IoT systems. He had also explored several problems related to distributed computing, big data reduction, reputation systems, federated learning, big/mobile/IoT data analytics, and IoT security.
Dr Rehman obtained his PhD in Distributed Computing in January 2017.
Research interests
- Federated Learning
- Decentralized AI
- Blockchain
- Privacy-enhancing Technologies
Teaching Expertise
- Programming
- Artificial Intelligence
- Distributed Systems
- Research Methodologies
Education/Academic qualification
PhD, Distributed Computing
Keywords
- T Technology
- Federated Leaning
- Decentralised AI
- Blockchain
- Edge Computing
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Collaborations and top research areas from the last five years
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A review of multi-agent deep reinforcement learning for resource allocation in beyond 5G network slicing: solutions, challenges and future research directions
Cui, Z., Qamar, F., Kazmi, S. H. A., Ariffin, K. A. Z., Safdar, G. A. & Rehman, M. H. U., 23 Mar 2026, In: PeerJ Computer Science. 12, p. 1-38 38 p., e3728.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Multi-agent deep reinforcement learning for resource allocation in beyond 5G network slicing: solutions, challenges and future research directions
Cui, Z., Qamar, F., Kazmi, S. H. A., Ariffin, K. A. Z., Safdar, G. & Rehman, M. H. U., 5 Feb 2026, (Accepted/In press) In: PeerJ Computer Science.Research output: Contribution to journal › Review article › peer-review
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Split averaging: bridging the heterogeneity gap in clients data for federated learning
Khan, S., Karetnikov, N., Rehman, M. H. U. & Svetinovic, D., 6 Feb 2026, In: IEEE Access. 14, p. 24018-24029 12 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Downloads (Pure) -
AI-enhanced landmark recognition for self-guided tour application using large language models
Karmaker, P., Korre, D., Rehman, M. H. U. & khodadadzadeh, M., 21 Sept 2025, MobileHCI '25 Adjunct: Adjunct Proceedings of the 27th International Conference on Mobile Human-Computer Interaction. Abdelrahman, Y., Elagroudy, P. & Alt, F. (eds.). Association for Computing Machinery, 18. (MobileHCI 2025 - Adjunct Proceedings of the 2025 Conference on Mobile Human-Computer Interaction).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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An investigation on machine learning models for enhanced thyroid prediction
Biswas, M. I., Feng, X., Rehman, M. H. U., Ihsan, M. & Bashir, A. K., 6 Nov 2024, Proceedings of Fourth International Conference on Computing and Communication Networks, ICCCN 2024. Kumar, A., Swaroop, A. & Shukla, P. (eds.). p. 511-524 14 p. (Lecture Notes in Networks and Systems; vol. 1293).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review