379 citations · 866 across the 40 of their papers we have counts for
9 papers · 1 filter
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
Mahmoud Nazzal, Abdallah Khreishah, Joyoung Lee +3
Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promisin…
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…
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan +2
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent se…
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
Ihab Mohammed, Shadha Tabatabai, Ala Al-Fuqaha +4
Machine Learning (ML), and Deep Learning (DL) in particular, play a vital role in providing smart services to the industry. These techniques however suffer from privacy and securit…
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services
Basheer Qolomany, Kashif Ahmad, Ala Al-Fuqaha +1
Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical…
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services
Basheer Qolomany, Ihab Mohammed, Ala Al-Fuqaha +2
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important. In…
SDN Flow Entry Management Using Reinforcement Learning
Ting-Yu Mu, Ala Al-Fuqaha, Khaled Shuaib +2
Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of datacenter networks (DCNs)…
Path Planning in Support of Smart Mobility Applications using Generative Adversarial Networks
Mehdi Mohammadi, Ala Al-Fuqaha, Jun-Seok Oh
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigati…