3 citations · 7 across the 12 of their papers we have counts for
13 papers
Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid
Ildar N. Idrisov, Divine Okeke, Abdullatif Albaseer +2
Modern power grids are transitioning towards power electronics-dominated grids (PEDG) due to the increasing integration of renewable energy sources and energy storage systems. This…
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach
Noor Aboueleneen, Yahuza Bello, Abdullatif Albaseer +3
Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful comput…
NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models
Asmaa Abdallah, Abdullatif Albaseer, Abdulkadir Celik +2
The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the compl…
Optimized Federated Multitask Learning in Mobile Edge Networks: A Hybrid Client Selection and Model Aggregation Approach
Moqbel Hamood, Abdullatif Albaseer, Mohamed Abdallah +2
We propose clustered federated multitask learning to address statistical challenges in non-independent and identically distributed data across clients. Our approach tackles complex…
Charging Ahead: A Hierarchical Adversarial Framework for Counteracting Advanced Cyber Threats in EV Charging Stations
Mohammed Al-Mehdhar, Abdullatif Albaseer, Mohamed Abdallah +1
The increasing popularity of electric vehicles (EVs) necessitates robust defenses against sophisticated cyber threats. A significant challenge arises when EVs intentionally provide…
Energy-Aware Service Offloading for Semantic Communications in Wireless Networks
Hassan Saadat, Abdullatif Albaseer, Mohamed Abdallah +2
Today, wireless networks are becoming responsible for serving intelligent applications, such as extended reality and metaverse, holographic telepresence, autonomous transportation,…
Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach
Moqbel Hamood, Abdullatif Albaseer, Mohamed Abdallah +1
Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non in…
Tailoring Semantic Communication at Network Edge: A Novel Approach Using Dynamic Knowledge Distillation
Abdullatif Albaseer, Mohamed Abdallah
Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over…
The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
Nima Abdi, Abdullatif Albaseer, Mohamed Abdallah
As smart grids (SG) increasingly rely on advanced technologies like sensors and communication systems for efficient energy generation, distribution, and consumption, they become en…
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
Shawqi Al-Maliki, Adnan Qayyum, Hassan Ali +5
Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial…
Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +3
Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distribu…
Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation
Shawqi Al-Maliki, Faissal El Bouanani, Mohamed Abdallah +2
Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applicatio…
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
Shadha Tabatabai, Ihab Mohammed, Basheer Qolomany +4
Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However…