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Muhammad Asad 0002
Person information
- affiliation: University of Tokyo, Department of Creative Informatics, Japan
- affiliation (PhD 2022): Nagoya Institute of Technology, Department of Computer Science, Japan
- affiliation (former): Dalian University of Technology, China
Other persons with the same name
- Muhammad Asad — disambiguation page
- Muhammad Asad 0001
— King's College London, UK (and 3 more)
- Muhammad Asad 0003
- Muhammad Asad 0004 — International Islamic University, Islamabad, Pakistan
- Muhammad Asad 0005 — FAST National University of Computer and Emerging Sciences, Department of Computer Science, Karachi, Pakistan
- Muhammad Asad 0006 — National University of Computer and Emerging Sciences, Department of Computer Sciences, Islamabad, Pakistan
- Muhammad Asad 0007 — Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
- Muhammad Asad 0008
- Muhammad Asad 0009 — Information Technology University of the Punjab, Lahore, Pakistan
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2020 – today
- 2024
- [j17]Muhammad Asad, Safa Otoum:
Federated learning for efficient spectrum allocation in open RAN. Clust. Comput. 27(8): 11237-11247 (2024) - [j16]Muhammad Asad
Secure and Efficient Blockchain-Based Federated Learning Approach for VANETs. IEEE Internet Things J. 11(5): 9047-9055 (2024) - [j15]Muhammad Asad
Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use Case. IEEE Trans. Netw. Serv. Manag. 21(4): 3759-3774 (2024) - [j14]Muhammad Asad, Safa Otoum:
Integrative Federated Learning and Zero-Trust Approach for Secure Wireless Communications. IEEE Wirel. Commun. 31(2): 14-20 (2024) - [c15]Ryo Iwaki, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Kazuki Maruta, Manabu Tsukada, Hideya Ochiai, Hiroshi Esaki:
Optimizing mmWave Beamforming for High-Speed Connected Autonomous Vehicles: An Adaptive Approach. CCNC 2024: 1088-1089 - 2023
- [j13]Muhammad Hamza Rafique Bhatti, Nadeem Javaid
New Hybrid Deep Learning Models to Predict Cost From Healthcare Providers in Smart Hospitals. IEEE Access 11: 136988-137010 (2023) - [j12]Muhammad Asad
Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey. Sensors 23(17): 7358 (2023) - [c14]Muhammad Asad, Safa Otoum:
Towards Privacy-Aware Federated Learning for User-Sensitive Data. BCCA 2023: 343-350 - [c13]Zekun Wang, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Manabu Tsukada:
Overcoming Environmental Challenges in CAVs through MEC-based Federated Learning. ICUFN 2023: 151-156 - [c12]Naren Bao
Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification. IoT 2023: 270-275 - [c11]Yusuke Sugizaki, Hideya Ochiai, Muhammad Asad, Manabu Tsukada, Hiroshi Esaki:
Wireless Ad-Hoc Federated Learning for Cooperative Map Creation and Localization Models. WF-IoT 2023: 1-6 - 2022
- [j11]Mian Muhammad Sadiq Fareed
ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans. IEEE Access 10: 96930-96951 (2022) - [j10]Muhammad Asad, Muhammad Aslam, Syeda Fizzah Jilani, Saima Shaukat, Manabu Tsukada:
SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems. Future Internet 14(11): 338 (2022) - [j9]Ayesha Shafique, Muhammad Asad, Muhammad Aslam, Saima Shaukat, Guo Cao:
Multi-hop similarity-based-clustering framework for IoT-Oriented Software-Defined wireless sensor networks. IET Wirel. Sens. Syst. 12(2): 67-80 (2022) - [j8]Muhammad Asad, Ahmed Moustafa, Fethi A. Rabhi, Muhammad Aslam:
THF: 3-Way Hierarchical Framework for Efficient Client Selection and Resource Management in Federated Learning. IEEE Internet Things J. 9(13): 11085-11097 (2022) - [j7]Ayesha Shafique, Guo Cao, Zia Khan, Muhammad Asad, Muhammad Aslam:
Deep Learning-Based Change Detection in Remote Sensing Images: A Review. Remote. Sens. 14(4): 871 (2022) - [j6]Muhammad Aslam, Dengpan Ye, Aqil Tariq, Muhammad Asad, Muhammad Hanif, David Ndzi, Samia Allaoua Chelloug, Mohamed Abd Elaziz, Mohammed Abdulaziz Aide Al-qaness, Syeda Fizzah Jilani:
Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT. Sensors 22(7): 2697 (2022) - [c10]Muhammad Asad, Safa Otoum, Saima Shaukat:
Resource and Heterogeneity-aware Clients Eligibility Protocol in Federated Learning. GLOBECOM 2022: 1140-1145 - 2021
- [j5]Muhammad Asad, Ahmed Moustafa, Muhammad Aslam:
CEEP-FL: A comprehensive approach for communication efficiency and enhanced privacy in federated learning. Appl. Soft Comput. 104: 107235 (2021) - [c9]Ahmed Moustafa, Muhammad Asad
PPCSA: Partial Participation-Based Compressed and Secure Aggregation in Federated Learning. AINA (2) 2021: 345-357 - [c8]Muhammad Asad, Ahmed Moustafa, Takayuki Ito, Muhammad Aslam:
Evaluating the Communication Efficiency in Federated Learning Algorithms. CSCWD 2021: 552-557 - [c7]TagyAldeen Mohamed, Ahmed Moustafa, Takayuki Ito, Muhammad Asad
Toward Agent-based Interactive Systems to Support Rehabilitation Process. CSCWD 2021: 558-562 - [i2]Muhammad Asad, Ahmed Moustafa, Takayuki Ito:
Federated Learning Versus Classical Machine Learning: A Convergence Comparison. CoRR abs/2107.10976 (2021) - 2020
- [j4]Ayesha Shafique, Guo Cao, Muhammad Aslam, Muhammad Asad, Dengpan Ye:
Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT). Sensors 20(12): 3521 (2020) - [j3]Muhammad Asad
A Critical Evaluation of Privacy and Security Threats in Federated Learning. Sensors 20(24): 7182 (2020) - [c6]Muhammad Aslam, Dengpan Ye, Muhammad Hanif, Muhammad Asad:
Machine Learning Based SDN-enabled Distributed Denial-of-Services Attacks Detection and Mitigation System for Internet of Things. ML4CS (1) 2020: 180-194 - [c5]Muhammad Aslam, Dengpan Ye, Muhammad Hanif, Muhammad Asad:
Adaptive Machine learning: A Framework for Active Malware Detection. MSN 2020: 57-64 - [i1]Muhammad Asad, Ahmed Moustafa, Takayuki Ito, Muhammad Aslam:
Evaluating the Communication Efficiency in Federated Learning Algorithms. CoRR abs/2004.02738 (2020)
2010 – 2019
- 2019
- [j2]Muhammad Asad, Muhammad Aslam, Nianmin Yao, Naeem Ayoub
IoT enabled adaptive clustering based energy efficient routing protocol for wireless sensor networks. Int. J. Ad Hoc Ubiquitous Comput. 32(2): 133-145 (2019) - 2018
- [c4]Muhammad Asad, Muhammad Aslam:
Heuristic Path-Reconfiguration Algorithm Using Multi-hop Opportunistic Routing in WSNs. FIT 2018: 47-52 - 2017
- [j1]Muhammad Aslam, Fan Wang, Xiaopeng Hu, Muhammad Asad, Ehsan Ullah Munir:
Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks. J. Sensors 2017: 5378403:1-5378403:14 (2017) - [c3]Muhammad Aslam, Fan Wang, Zefeng Lv, Muhammad Asad, Samra Zafar, Ehsan Ullah Munir, Xiaopeng Hu:
Energy Efficient Cubical layered Path Planning Algorithm (EECPPA) for acoustic UWSNs. PACRIM 2017: 1-6 - [c2]Naeem Ayoub
MAHEE: Multi-hop advance heterogeneity-aware energy efficient path planning algorithm for wireless sensor networks. PACRIM 2017: 1-6 - 2014
- [c1]Muhammad Aslam, Ehsan Ullah Munir, Muhammad Bilal, Muhammad Asad, Asad Ali, Tauseef Shah, Syed Bilal:
HADCC: Hybrid Advanced Distributed and Centralized Clustering Path Planning Algorithm for WSNs. AINA 2014: 657-664
Coauthor Index
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