141 citations · 210 across the 8 of their papers we have counts for
8 papers
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities
Osama Wehbi, Sarhad Arisdakessian, Mohsen Guizani +5
Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban…
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
Hani Sami, Ahmad Hammoud, Mouhamad Arafeh +14
The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual…
A Survey on Explainable Artificial Intelligence for Cybersecurity
Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab +5
The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) i…
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +4
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) f…
Trends, Politics, Sentiments, and Misinformation: Understanding People's Reactions to COVID-19 During its Early Stages
Omar Abdel Wahab, Ali Mustafa, André Bertrand Abisseck Bamatakina
The sudden outbreak of COVID-19 resulted in large volumes of data shared on different social media platforms. Analyzing and visualizing these data is doubtlessly essential to havin…
Cloud computing as a platform for monetizing data services: A two-sided game business model
Ahmed Saleh Bataineh, Jamal Bentahar, Rabeb Mizouni +3
With the unprecedented reliance on cloud computing as the backbone for storing today's big data, we argue in this paper that the role of the cloud should be reshaped from being a p…
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
Omar Abdel Wahab, Jamal Bentahar, Robin Cohen +2
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in…
Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems
Adel Abusitta, Esma Aïmeur, Omar Abdel Wahab
In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in th…