Dr Muhammad Asad
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
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

Journals
[J21] Muhammad Hamza Rafique Bhatti, Nadeem Javaid, Nabil Alrajeh, Muhammad Aslam, Muhammad Asad, "New hybrid deep learning models to predict cost from healthcare providers in smart hospitals," in IEEE Access

[J20] Asad, Muhammad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Naren Bao, and Manabu Tsukada, "Secure and Efficient Blockchain-Based Federated Learning Approach for VANETs," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3322221.

[19] Asad, Muhammad, Saima Shaukat, Dou Hu, Zekun Wang, Ehsan Javanmardi, Jin Nakazato, and Manabu Tsukada. 2023. "Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey" Sensors 23, no. 17: 7358. https://doi.org/10.3390/s23177358 

[18] Asad, Muhammad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, and Manabu Tsukada. 2023. "A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems" Applied Sciences 13, no. 10: 6201. https://doi.org/10.3390/app13106201

[17] Shaukat, Saima, Asad, Muhammad, and Asmara Akram. 2023. "Developing an Urdu Lemmatizer Using a Dictionary-Based Lookup Approach" Applied Sciences 13, no. 8: 5103. https://doi.org/10.3390/app13085103

[16] Asad, Muhammad, Muhammad Aslam, Syeda Fizzah Jilani, Saima Shaukat, and Manabu Tsukada. 2022. "SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems" Future Internet 14, no. 11: 338. https://doi.org/10.3390/fi14110338

[J15] Ahmed, Gulnaz, Meng Joo Er, Mian Muhammad Sadiq Fareed, Shahid Zikria, Saqib Mahmood, Jiao He, Muhammad Asad, Syeda Fizzah Jilani, and Muhammad Aslam. "DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network." Molecules 27, no. 20 (2022): 7085. 

[J14] Fareed, Mian Muhammad Sadiq, Shahid Zikria, Gulnaz Ahmed, Saqib Mahmood, Muhammad Aslam, Syeda Fizzah Jillani, Ahmad Moustafa, and Muhammad Asad. "ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans." IEEE Access (2022). 

[J13] Aslam, Muhammad, Dengpan Ye, Aqil Tariq, Muhammad Asad, Muhammad Hanif, David Ndzi, Samia A. Chelloug, Mohamed A. Elaziz, Mohammed A.A. Al-Qaness, and Syeda F. Jilani. 2022. "Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT" Sensors 22, no. 7: 2697. https://doi.org/10.3390/s22072697 

[J12] Ayesha Shafique, Muhammad Asad, Muhammad Aslam, Saima Shaukat, and Guo Cao “Multi-Hop Similarity-based Clustering Framework for IoT-oriented Software Defined Wireless Sensor Networks“ in IET Wireless Sensor Systems.

[J11] Shafique, Ayesha, Guo Cao, Zia Khan, Muhammad Asad, and Muhammad Aslam. 2022. "Deep Learning-Based Change Detection in Remote Sensing Images: A Review" Remote Sensing 14, no. 4: 871.

[J10] Asad, Muhammad, Ahmed Moustafa, Fethi A. Rabhi, and Muhammad Aslam. "THF: 3-way hierarchical framework for efficient client selection and resource management in federated learning." IEEE Internet of Things Journal 9, no. 13 (2021): 11085-11097.

[J9] Asad Muhammad, Ahmed Moustafa, and Muhammad Aslam. "CEEP-FL: A comprehensive approach for communication efficiency and enhanced privacy in federated learning." Applied Soft Computing (2021): 107235.

[J8] Asad Muhammad; Moustafa, Ahmed; Yu, Chao. 2020. "A Critical Evaluation of Privacy and Security Threats in Federated Learning." Sensors 20, no. 24: 7182.

[J7] Ayesha Shafique, Guo Cao, Muhammad Aslam, Muhammad Asad, and Dengpan Ye; Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT). Sensors 2020, 20, 3521.

[J6] Asad Muhammad; Moustafa, Ahmed; Ito, Takayuki. 2020. "FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning." Appl. Sci. 10, no. 8: 2864.

[J5] Muhammad Aslam, Fan Wang, Xiaopeng Hu, Muhammad Asad, and Ehsan Ullah Munir, “Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks,” Journal of Sensors, vol. 2017, Article ID 5378403, 14 pages, 2017.

[J4] Asad Muhammad, et al. "IoT enabled adaptive clustering based energy efficient routing protocol for wireless sensor networks." International Journal of Ad Hoc and Ubiquitous Computing 32.2 (2019): 133-145.

[J3] Muhammad Asad, Yao Nianmin, and Muhammad Aslam. "Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs." Technologies 6.1 (2018): 35.

[J2] Muhammad Asad, Arsalan Ali Shaikh, Soomro Pir Dino, Muhammad Aslam, and Yao Nianmin, "Lifetime Maximization on Scalable Stable Election Protocol for Large Scale Traffic Engineering" International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018.

[J1] Christopher Mumpe, Da Tang, Muhammad Asad, Jing Chen, Jinsi Zhu, and Luyuan jin, "Neighbour-Cooperation Heterogeneity-Aware Traffic Engineering For Wireless Sensor Networks" International Journal of Advanced Computer Science and Applications(IJACSA), 9(12), 2018.


Chapters
[B2] Moustafa A., Asad Muhammad., Shaukat S., Norta A. (2021) PPCSA: Partial Participation-Based Compressed and Secure Aggregation in Federated Learning. In: Barolli L., Woungang I., Enokido T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_28

[B1] Aslam, Muhammad, Dengpan Ye, Muhammad Hanif, and Muhammad Asad. "Machine learning based SDN-enabled distributed denial-of-services attacks detection and mitigation system for Internet of Things." In International Conference on Machine Learning for Cyber Security, pp. 180-194. Springer, Cham, 2020.
Conference Papers

[C11] Yusuke Sugizaki, Hideya Ochiai, Muhammad Asad, Manabu Tsukada, Hiroshi Esaki, "Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models," In The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023), Aveiro, Portugal, 2023.
[C10] Wang, Zekun, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, and Manabu Tsukada. "Overcoming Environmental Challenges in CAVs through MEC-based Federated Learning." In 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 151-156. IEEE, 2023.
[C9] Asad, Muhammad, Safa Otoum, and Saima Shaukat. "Resource and Heterogeneity-aware Clients Eligibility Protocol in Federated Learning." In GLOBECOM 2022-2022 IEEE Global Communications Conference, pp. 1140-1145. IEEE, 2022.
[C8] Muhammad Asad, Ahmed Moustafa, and Takayuki Ito "Federated Learning Verses Classical Machine Learning: A Convergence Comparison" The 15th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2020), Tasmania, Australia (Online), 25-27 November 2020. (yet to publish)
[C7] Aslam, Muhammad, Dengpan Ye, Muhammad Hanif, and Muhammad Asad. "Adaptive Machine learning: A Framework for Active Malware Detection." In 2020 16th International Conference on Mobility, Sensing and Networking (MSN), pp. 57-64. IEEE, 2020.
[C6] Asad, Muhammad, Ahmed Moustafa, Takayuki Ito, and Muhammad Aslam. "Evaluating the communication efficiency in federated learning algorithms." In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 552-557. IEEE, 2021.
[C5] Mohamed, Tagy Aldeen, Ahmed Moustafa, Takayuki Ito, and Muhammad Asad. "Toward Agent-based Interactive Systems to Support Rehabilitation Process." In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 558-562. IEEE, 2021.
[C4] Asad, Muhammad, and Muhammad Aslam. "Heuristic Path-Reconfiguration Algorithm Using Multi-hop Opportunistic Routing in WSNs." In 2018 International Conference on Frontiers of Information Technology (FIT), pp. 47-52. IEEE, 2018.
[C3] Ayoub, Naeem, Muhammad Asad, Muhammad Aslam, Zhenguo Gao, Ehsan Ullah Munir, and Rachida Tobji. "MAHEE: Multi-hop advance heterogeneity-aware energy efficient path planning algorithm for wireless sensor networks." In Communications, Computers and Signal Processing (PACRIM), 2017 IEEE Pacific Rim Conference on, pp. 1-6. IEEE, 2017.
[C2] Aslam, Muhammad, Fan Wang, Zefeng Lv, Muhammad Asad, Samra Zafar, Ehsan Ullah Munir, and Xiaopeng Hu. "Energy Efficient Cubical layered Path Planning Algorithm (EECPPA) for acoustic UWSNs." In Communications, Computers and Signal Processing (PACRIM), 2017 IEEE Pacific Rim Conference on, pp. 1-6. IEEE, 2017.
[C1] Aslam, Muhammad, Ehsan Ullah Munir, Muhammad Bilal, Muhammmad Asad, Asad Ali, Tauseef Shah, and Syed Bilal. "HADCC: hybrid advanced distributed and centralized clustering path planning algorithm for WSNs." In Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on, pp. 657-664. IEEE, 2014.
Presentations and posters

[P1] Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Manabu Tsukada "An Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management" in 2023 EuCNC & 6G Summit.