Profiles
Dr Muhammad Asad
Lecturer in Computer Science
School of Engineering, Computing and Mathematics (Faculty of Science and Engineering)
Biography
Biography
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.
Qualifications
Ph.D.: Nagoya Institute of Technology, Japan
Masters: Dalian University of Technology, China
Bachelors: COMSATS University Islamabad, WAH Campus, Pakistan
Masters: Dalian University of Technology, China
Bachelors: COMSATS University Islamabad, WAH Campus, Pakistan
Research
Research
Research interests
Federated Learning,
Deep Learning,
Software-Defined Networking (SDN),
Wireless Sensor Networks (WSNs),
Vehicular Ad-Hoc Networks (VANets),
Internet of Things (IoT).
Publications
Publications
Key publications
Key publications are highlighted
Journals
Articles
(2024) 'Integrative Federated Learning and Zero-Trust Approach for Secure Wireless Communications' IEEE Wireless Communications 31, (2) 14-20 , DOI
(2023) 'Secure and Efficient Blockchain-Based Federated Learning Approach for VANETs' IEEE Internet of Things Journal 11, (5) 9047-9055 , DOI
(2023) 'Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey' Sensors (Basel) 23, (17) , DOI
(2023) 'A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems' Applied Sciences (Switzerland) 13, (10) , DOI
(2023) 'Developing an Urdu Lemmatizer Using a Dictionary-Based Lookup Approach' Applied Sciences (Switzerland) 13, (8) , DOI
(2023) 'New Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals' IEEE Access 11, 136988-137010 , DOI
(2022) 'SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems' Future Internet 14, (11) , DOI
(2022) 'DAD-Net: Classification of Alzheimer's Disease Using ADASYN Oversampling Technique and Optimized Neural Network' Molecules 27, (20) , DOI
(2022) 'A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images' Biomedicines 10, (9) , DOI
(2022) 'Multi-hop similarity-based-clustering framework for IoT-Oriented Software-Defined wireless sensor networks' IET Wireless Sensor Systems 12, (2) 67-80 , DOI
(2022) 'Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT' Sensors (Basel) 22, (7) , DOI
(2022) 'Deep Learning-Based Change Detection in Remote Sensing Images: A Review' Remote Sensing 14, (4) , DOI
(2022) 'ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans' IEEE Access 10, 96930-96951 , DOI
(2021) 'THF: 3-Way Hierarchical Framework for Efficient Client Selection and Resource Management in Federated Learning' IEEE Internet of Things Journal 9, (13) 11085-11097 , DOI
(2021) 'CEEP-FL: A comprehensive approach for communication efficiency and enhanced privacy in federated learning' Applied Soft Computing 104, , DOI
(2020) 'A critical evaluation of privacy and security threats in federated learning' Sensors (Switzerland) 20, (24) 1-15 , DOI
(2020) 'Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)' Sensors (Basel) 20, (12) , DOI
(2020) 'FedOpt: Towards communication efficiency and privacy preservation in federated learning' Applied Sciences (Switzerland) 10, (8) , DOI
(2019) 'IoT enabled adaptive clustering based energy efficient routing protocol for wireless sensor networks' International Journal of Ad Hoc and Ubiquitous Computing 32, (2) 133-145 , DOI
(2018) 'Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs' Technologies 6, (1) , DOI
(2018) 'Lifetime maximization on Scalable Stable Election Protocol for Large Scale traffic engineering' International Journal of Advanced Computer Science and Applications 9, (1) 412-418 , DOI
(2017) 'Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks' Journal of Sensors 2017, , DOI
Conference Papers
(2023) 'Overcoming Environmental Challenges in CAVs through MEC-based Federated Learning' 151-156 , DOI
(2022) 'Resource and Heterogeneity-aware Clients Eligibility Protocol in Federated Learning' 1140-1145 , DOI
(2021) 'PPCSA: Partial Participation-Based Compressed and Secure Aggregation in Federated Learning' 345-357 , DOI
(2020) 'Machine Learning Based SDN-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things' 180-194 , DOI
(2018) 'Heuristic path-reconfiguration algorithm using multi-hop opportunistic routing in WSNs' 47-52 , DOI
(2017) 'Energy efficient cubical layered path planning algorithm (EECPPA) for acoustic UWSNs' 1-6 , DOI
(2017) 'MAHEE: Multi-hop advance heterogeneity-aware energy efficient path planning algorithm for wireless sensor networks' 1-6 , DOI
(2014) 'HADCC: Hybrid advanced distributed and centralized clustering path planning algorithm for WSNs' 657-664 , DOI