Dr. Muhammad Asad currently holds the position of Lecturer at the University of Plymouth, UK, and simultaneously 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 Ph.D. and master’s degrees are supported by scholarships from the Japanese (MEXT) and Chinese (CSC) governments, respectively. 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.
Muhammad Asad
- Website
- sites.google.com/view/asadhijaz/home
- muhammad.asad@plymouth.ac.uk
asad@g.ecc.u-tokyo.ac.jp
Publications (11)
JournalFederated Learning for Secure and Efficient Vehicular Communications in Open RAN
Workshop / WiPA Research of Kalman Filter enabled Beam Tracking for Multiple Vehicles
JournalSecure and Efficient Blockchain-based Federated Learning Approach For VANETs
Workshop / WiPTowards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification
Int'l conferenceWireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models
DomesticExtended Kalman filter based beam tracking for vehicle position and velocity estimation under intersection scenario
Dou Hu, Jin Nakazato, Ehsan Javanmardi, Muhammad Asad, Manabu Tsukada, Kazuki Maruta — 革新的無線通信技術に関する横断型研究会（MIKA）, 沖縄県, 2023
JournalLimitations and Future Aspects of Communication Costs in Federated Learning: A Survey
Int'l conferenceOvercoming Environmental Challenges in CAVs Through MEC-Based Federated Learning
PosterAn Extended Kalman Filter Enabled Beam Tracking Framework in Intersection Management
JournalA Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems