Search Results - "Wehbi, Osama"

Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities by Wehbi, Osama, Arisdakessian, Sarhad, Guizani, Mohsen, Wahab, Omar Abdel, Mourad, Azzam, Otrok, Hadi, khzaimi, Hoda Al, Ouni, Bassem

“…Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent…”
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FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices by Wehbi, Osama, Arisdakessian, Sarhad, Wahab, Omar Abdel, Otrok, Hadi, Otoum, Safa, Mourad, Azzam, Guizani, Mohsen

“…Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet…”
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Trustworthy Hierarchical Federated Learning for Digital Healthcare by Arisdakessian, Sarhad, Wahab, Omar Abdel, Wehbi, Osama, Mourad, Azzam, Otrok, Hadi

“…Healthcare institutions and medical device manufacturers are under regulatory obligations to safeguard and protect the privacy of data they acquire from…”
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Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory by Wehbi, Osama, Arisdakessian, Sarhad, Wahab, Omar Abdel, Otrok, Hadi, Otoum, Safa, Mourad, Azzam

“…Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. However, selecting…”
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Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities by Wehbi, Osama, Arisdakessian, Sarhad, Guizani, Mohsen, Wahab, Omar Abdel, Mourad, Azzam, Otrok, Hadi, khzaimi, Hoda Al, Ouni, Bassem

“…Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent…”
Get full text

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices by Wehbi, Osama, Arisdakessian, Sarhad, Wahab, Omar Abdel, Otrok, Hadi, Otoum, Safa, Mourad, Azzam, Guizani, Mohsen

“…Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet…”
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Towards Mutual Trust-Based Matching For Federated Learning Client Selection by Wehbi, Osama, Wahab, Omar Abdel, Mourad, Azzam, Otrok, Hadi, Alkhzaimi, Hoda, Guizani, Mohsen

“…Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a…”
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The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions by Sami, Hani, Hammoud, Ahmad, Arafeh, Mouhamad, Wazzeh, Mohamad, Arisdakessian, Sarhad, Chahoud, Mario, Wehbi, Osama, Ajaj, Mohamad, Mourad, Azzam, Otrok, Hadi, Wahab, Omar Abdel, Mizouni, Rabeb, Bentahar, Jamal, Talhi, Chamseddine, Dziong, Zbigniew, Damiani, Ernesto, Guizani, Mohsen

“…The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive…”
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