Research output per year
Research output per year
Overview
Profile summary
Dr. Muhammad Asad holds the position of Lecturer in Computer Science at the University of Plymouth, UK. Previously, he served as a Visiting Researcher and Project Researcher in the Department of Creative Informatics at the University of Tokyo. Dr. Asad completed his Ph.D. in Computer Science at the Nagoya Institute of Technology, Japan, where he received the prestigious President’s Award in 2021–2022 for his outstanding research achievements. Earlier in his academic journey, Dr. Asad earned a Master’s degree in Computer Science from Dalian University of Technology, China, and a Bachelor’s degree in Telecommunication and Networking from COMSATS University Islamabad, Pakistan. His research primarily focuses on Federated Learning, Deep Learning, and a range of advanced networking technologies, including VANETs, ITS, WSNs, SDN, and IoT, showcasing his deep expertise and ongoing contributions to the field of computer science.
Professional memberships
Fellow of the Higher Education Academy (FHEA), Advance HE, United Kingdom (Awarded 2025)
Teaching interests
Teaching modules:
- COMP1000 - Software Engineering 1
- COMP3007 - HCI, Usability and Visualization
His broader teaching interests include Software Engineering, Programming Fundamentals, Machine Learning, and Computer Networks.
Fingerprint
Dive into the research topics where Muhammad Asad is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Research output
-
Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use-Case
Asad, M., Otoum, S. & Shaukat, S., 2024, In: IEEE Transactions on Network and Service Management. 21, 4, p. 3759-3774 16 p.Research output: Contribution to journal › Article › peer-review
-
Federated learning for efficient spectrum allocation in open RAN
Asad, M. & Otoum, S., Nov 2024, In: Cluster Computing. 27, 8, p. 11237-11247 11 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Downloads (Pure) -
Integrative Federated Learning and Zero-Trust Approach for Secure Wireless Communications
Asad, M. & Otoum, S., 1 Apr 2024, In: IEEE Wireless Communications. 31, 2, p. 14-20 7 p.Research output: Contribution to journal › Article › peer-review
-
Secure and Efficient Blockchain-Based Federated Learning Approach for VANETs
Asad, M., Shaukat, S., Javanmardi, E., Nakazato, J., Bao, N. & Tsukada, M., 1 Mar 2024, In: IEEE Internet of Things Journal. 11, 5, p. 9047-9055 9 p.Research output: Contribution to journal › Article › peer-review
-
6GENABLERS: A Holistic Approach to Establish Pervasive Trust in 6G Networks
Asad, M., Fernandez-Fernandez, A., Chergui, H., Compastie, M., Montagud, M., Fernandez, S. & Siddiqui, S., 2023, 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2023. Institute of Electrical and Electronics Engineers Inc., p. 55-60 6 p. (IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD).Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings published in a book › peer-review