Short Biography
Dr. Muhammad Asad completed his Ph.D. in Computer Science from the Department of Computer Science, Nagoya Institute of Technology, Japan. He received a 3-year MS degree in Computer Science (MSCS) and a 4-year BS degree in Telecommunication and Networking (BSTN) from Dalian University of Technology, Dalian, China, and COMSATS University Islamabad, WAH Campus, Pakistan, in June 2018 and June 2014, respectively. He received the Chinese Government Scholarship (CSC) from 2015-2018. He also received the Japanese Government Scholarship (MEXT) 2019-2022. His primary research interests include Federated Learning, Deep Learning, Software-Defined Networking (SDN), Wireless Sensor Networks (WSNs), Vehicular Ad-Hoc Networks (VANets), and the Internet of Things (IoT).
Link to his profile on professional sites:
LinkedIn: Asad Hijaz
ORCID ID: 0000-0003-0036-1714
Scopus: 57210681680
Web of Science Researcher-ID: JKJ-4191-2023
Educational Background
PhD in Computer Science
Nagoya Institute of Technology, Japan
April 2020 - June 2022
Master of Science (MS) in Computer Science and Technology
Dalian University of Technology, China
Sept 2015 - June 2018
Bachelors of Science (BS) in Telecommunication and Networking (BSTN)
COMSATS University Islamabad, Wah Campus
Sept 2010 - June 2014
Ph.D. Thesis
Title: Expanding Federated Learning by Several Strategies to Reduce the Communication Costs
MS Thesis
Title: Optimized Routing Protocols for Optimal Data Extraction of Static and Spiral Mobility based WSNs
Published: Technologies, MDPI
BS Final Year Project
Title: HADCC: Hybrid Advanced Distributed and Centralized Clustering Path Planning Algorithm for WSNs
Published: Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference
Research Interests
Communication Efficiency in Federated Learning
Privacy Preserving Algorithms in Federated Learning
Distributed Machine Learning based Vehicular Ad-Hoc Networks
Energy optimisation and Path Planning Algorithms in WSNs
Wireless Protocol Mobility Management for VANET, WSN
Path Planning for Acoustic Under-Water WSNs