13 citations · 26 across the 15 of their papers we have counts for
16 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…
Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning
Moqbel Hamood, Abdullatif Albaseer, Mohamed Abdallah +1
Clustered Federated Multi-task Learning (CFL) has emerged as a promising technique to address statistical challenges, particularly with non-independent and identically distributed…
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…
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…
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…
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…
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to obtain reliable specialized models when data is imbalanced and distributed in a non-i.i.d. (no…
Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey
Marwan Dhuheir, Abdullatif Albaseer, Emna Baccour +3
Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emot…
Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leadin…
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user's privacy, reduces the communication cos…
Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
Bekir Sait Ciftler, Abdullatif Albaseer, Noureddine Lasla +1
Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to i…
Exploiting Unlabeled Data in Smart Cities using Federated Learning
Abdullatif Albaseer, Bekir Sait Ciftler, Mohamed Abdallah +1
Privacy concerns are considered one of the main challenges in smart cities as sharing sensitive data brings threatening problems to people's lives. Federated learning has emerged a…