Mohamed Elmahallawy

• Ph.D. in Computer Science, Missouri University of Science and Technology, 2024
• M.S. in Computer Science and Electrical Engineering, University of Rostock, 2019
• B.S. in Electronics and Communications Engineering, Higher Institute of Engineering in Elshorouk City, 2012

Joined WSU Tri-Cities in 2024 from Missouri University of Science and Technology.

Teaching

• Introduction to Machine Learning

Research

• Advanced Machine/Federated Learning Techniques
• Robust Cybersecurity Measures and Encryption
• Integration and Optimization of Internet of Things

• M. Elmahallawy and T. Luo, “Secure and Privacy-Preserving Federated Learning for Low Earth Orbit Satellite Networks”, IEEE Transactions on Dependable and Secure Computing, Under review.
• M. Elmahallawy, T. Luo, and K. Ramadan, “Efficient Federated Learning for LEO Satellite Networks Integrated with Unmanned High-altitude Platforms using hybrid NOMA-OFDMA”, IEEE Journal on Selected Areas in Communications (JSAC), 2024, DOI: 10.1109/JSAC.2024.3365885.
• M. Elmahallawy, T. Elfouly, A. Alouani, and A. M. Massoud, “A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction”, in IEEE Access, vol. 10, pp. 119040-119070, 2022.
• M. Elmahallawy, A. TagEldein, and S. Elagooz, “Performance Enhancement of Underwater Acoustic OFDM Communication Systems”, Wireless Personal Communications 108 (2019): 2047-2057.
• M. Elmahallawy and A. TagEldein, “Performance Enhancement of UWA-OFDM Communication Systems based on FWHT”, International Journal of Communication Systems 32.16 (2019): e3979.
• M. Elmahallawy, and Sanjay Madria, “FedMining: Efficient Federated Learning with Functional Encryption for Hazard Detection in Underground Mining”, IEEE International Conference on Computer Communications (INFOCOM 2025), under review.
• Md Sazedur Rahman, M. Elmahallawy, Sanjay Madria, and Samuel Frimpong, “CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks”, The 21st IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2024), under review.
• Manish Yadav, M. Elmahallawy, and Sanjay Madria, “Predicting Battery Levels in WSNs Using Reinforcement Learning in Harsh Underground Mining Environments”, The 43rd International Symposium on Reliable Distributed Systems (SRDS 2024), under review.
• Mizanur Jewel, M. Elmahallawy, and Sanjay Madria, “Predicting Battery Levels in WSNs Using Reinforcement Learning in Harsh Underground Mining Environments”, The 43rd International Symposium on Reliable Distributed Systems (SRDS 2024), under review.
• Shreen Gul, M. Elmahallawy, and Sanjay Madria, “LPLGrad: Loss Prediction Loss with Gradient Norm for instant labeling”, The IEEE BigData (Bigdata 2024), under review.
• M. Elmahallawy, and T. Luo, “Secure Aggregation Is Myopic: Preserving Long-Term Privacy in Asynchronous Federated Satellite Learning”, The 27 European Conference on Artificial Intelligence (ECAI), under review.
• M. Elmahallawy, and T. Luo, “Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning”, 22nd IEEE International Conference on Pervasive Computing and Communications (PerCom), March 2024.
• M. Elmahallawy, T. Luo, and M. I. Ibrahem, “Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation”, IEEE Global Communication Conference (GlobeCom), December 2023.
• M. Elmahallawy, and T. Luo, “One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes”, 24th IEEE International Conference on Mobile Data Management (MDM), July 2023.
• M. Elmahallawy, and T. Luo, “Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling”, IEEE Conference on Communications (ICC), 2023.
• M. Elmahallawy, and T. Luo, “AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms”, 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 5478-5487.
• M. Elmahallawy, and T. Luo, “FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs”, in Proc. IEEE 14th International Conference on Wireless Communication and Signal Process., Nanjing, China, 2022, pp. 1-6.
• Yasmine Mustafa, M. Elmahallawy, T. Luo, and Seif Eldawlatly “A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition”, IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 2023.