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. He also serves as a Visiting Researcher at the University of Tokyo. Prior to these roles, he worked as a 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.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Collaborations and top research areas from the last five years
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Research output
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Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use-Case
Asad, M., Otoum, S. & Shaukat, S., 2024, (Accepted/In press) In: IEEE Transactions on Network and Service Management. 21, 4, p. 3759-3774 16 p.Research output: Contribution to journal › Article › peer-review
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Federated learning for efficient spectrum allocation in open RAN
Asad, M. & Otoum, S., 2024, (Accepted/In press) In: Cluster Computing.Research output: Contribution to journal › Article › peer-review
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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
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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
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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