510 citations · 2.2k across the 62 of their papers we have counts for
11 papers · 1 filter
Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks
Muhammet Hevesli, Abegaz Mohammed Seid, Aiman Erbad +1
Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude p…
Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
Marwan Dhuheir, Aiman Erbad, Ala Al-Fuqaha
In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, s…
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems
Emna Baccour, Aiman Erbad, Amr Mohamed +2
Although Deep Neural Networks (DNN) have become the backbone technology of several ubiquitous applications, their deployment in resource-constrained machines, e.g., Internet of Thi…
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints
Ilyes Mrad, Lutfi Samara, Alaa Awad Abdellatif +3
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches.…
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
Alaa Awad Abdellatif, Naram Mhaisen, Zina Chkirbene +3
The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This c…
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data
Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed +4
Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. I…
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…
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
Naram Mhaisen, Alaa Awad, Amr Mohamed +2
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devic…
Feasibility of Supervised Machine Learning for Cloud Security
Deval Bhamare, Tara Salman, Mohammed Samaka +2
Cloud computing is gaining significant attention, however, security is the biggest hurdle in its wide acceptance. Users of cloud services are under constant fear of data loss, secu…