Viet Quoc Pham

Selected Publications

1. M.-D. Nguyen, S. H. Wanasekara, L.-T. Nguyen, K.-T. Yong, Q.-V. Pham, N. H. Tran, and D. D. Le, “Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.

2. L. T. Nguyen, M. T. de Oliveira, A. Braeken, A. Y. Ding, and Q.-V. Pham, “Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead,” IEEE Internet Computing, 2026.

3. M.-D. Nguyen, Q.-V. Pham, H. T. Dinh, L. Tran-Thanh, D. N. Nguyen, and W.-J. Hwang, “Exploiting Label-aware Knowledge from Heterogeneous Clients for Hierarchical Federated Learning,” IEEE Transactions on Parallel and Distributed Systems, 2026.

4. N.-M. Luu, M.-D. Nguyen, E. B. Mohamed, V.-D. Nguyen, D. T. Hoang, Diep N. Nguyen, and Q.-V. Pham, “Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control,” IEEE Transactions on Mobile Computing, 2026.

5. T. B. Nguyen, M.-D. Nguyen, J. Park, Q.-V. Pham, and W.-J. Hwang, “Federated Domain Generalization with Data-free On-server Matching Gradient,” in International Conference on Learning Representations (ICLR), 2025.

6. V.-T. Tran, H.-H. Nguyen-Le, and Q.-V. Pham, “ToFU: Transforming How Federated Learning Systems Forget User Data,” in European Conference on Artificial Intelligence (ECAI), 2025.

7. M. Le, H. T. Dinh, D. N. Nguyen, Q.-V. Pham, and W.-J. Hwang, “Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation,” IEEE Internet of Things Journal, 2024.

8. M. Le, T. Huynh-The, T. Do-Duy, T.-H. Vu, W.-J. Hwang, and Q.-V. Pham, “Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, 2024.

9. Y. M. Saputra, D. N. Nguyen, H. T. Dinh, Q.-V. Pham, E. Dutkiewicz, and W.-J. Hwang, “Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services,” IEEE Transactions on Mobile Computing, 2023.

10. M.-D. Nguyen, S.-M. Lee, Q.-V. Pham, H. T. Dinh, D. N. Nguyen, and W.-J. Hwang, “HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks,” IEEE Transactions on Mobile Computing, 2023.

11. Q.-V. Pham, M. Le, T. Huynh-The, Z. Han, and W.-J. Hwang, “Energy-Efficient Federated Learning over UAV-enabled Wireless Powered Communications,” IEEE Transactions on Vehicular Technology, 2022.

12. Q.-V. Pham, Hoang T. Nguyen, Z. Han, and W.-J. Hwang, “Coalitional Games for Computation Offloading in NOMA-Enabled Multi-Access Edge Computing,” IEEE Transactions on Vehicular Technology, 2020.

Academic Bio

I am an Assistant Professor (above the bar) in the Networks and Distributed Systems Discipline at the School of Computer Science and Statistics, Trinity College Dublin, Ireland. I am also affiliated with Research Ireland ADAPT Centre as an Academic Collaborator, ADVANCE CRT centre as an Academic Supervisor, and Research Ireland CONNECT Centre as an Associate Investigator. Prior to my tenure at TCD, I held various positions at top universities in Korea from 2017 to 2023: postdoc researcher at Kyung Hee University and research professor at Changwon National University and Pusan National University. I received the BSc and Ph.D. degree, both in Telecommunications, from Hanoi University of Science and Technology in 2013 and Inje University in 2017.

Broadly speaking, my research belongs to the areas of mobile edge networks and AI, the fields that are changing very dramatically and need a great effort from the research communities to realize a future 6G ecosystem in the 2030s. My research interest is based on multidisciplinary approaches of computer science, telecommunications, and engineering mathematics for the practical design of complex and communication networks. Based on that vision, I have led the COINLab and focused on three main thrusts, including COmputing (e.g., edge computing, aerial computing, edge of things, and edge AI), Intelligence (e.g., federated learning, machine unlearning, and quantum ML), and Networking (e.g., 6G wireless, network AI, and blockchain). My current research centres on decentralised machine learning, with a particular focus on data privacy, federated learning, and machine unlearning. Through an interdisciplinary approach, I aim to advance technologies that support distributed intelligence and reliable connectivity at scale and for all.

I was a recipient of the Best Ph.D. Dissertation Award from Inje University in 2017, the Top Reviewer Award from the IEEE Transactions on Vehicular Technology in 2020, the Golden Globe Award in Science and Technology for Younger Researchers in Vietnam in 2021, the IEEE ATC Best Paper Award in 2022, and the IEEE CE Magazine Best Paper Award in 2024. I was recognised as one of the most-cited researchers by Scopus for five consecutive years, from 2021 to 2025 and one of the Best Computer Science Scientists in Ireland by Research.com in 2024 and 2025. I have been selected as a Highly Cited Researcher (in Engineering, Telecommunications, and Computer Science) by Clarivate in 2024, and a Featured Author on IEEE Xplore in 2026, highlighting my recent work on verifiable federated unlearning. I was honoured with the IEEE ComSoc Best Young Researcher Award for EMEA 2023 in recognition of my research activities for the benefit of the Society.

I am currently serving as an Area Editor (and an Editor) of IEEE Communications Surveys & Tutorials, and an (Associate) Editor of IEEE Communications Letters, IEEE Communications Standards Magazine, IEEE Transactions on Mobile Computing, and IEEE Transactions on Network Science and Engineering. I am a senior member of the IEEE, the IEEE ComSoc, and the IEEE VTS.

About Me

In English naming style, my full name is Viet Quoc Pham, which I prefer to be called Viet. In Vietnamese, my full name is Phạm Quốc Việt. I use Quoc-Viet Pham as my author name in academic articles. Also, my TCD username is phamq, which is created based on my first name and middle name. I very often have problems answering the “What is your correct name?” question :)