Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities

no code implementations • 1 May 2024 • Osama Wehbi, Sarhad Arisdakessian, Mohsen Guizani, Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, Hoda Al khzaimi, Bassem Ouni

Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities.

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

no code implementations • 18 Apr 2023 • Hani Sami, Ahmad Hammoud, Mouhamad Arafeh, Mohamad Wazzeh, Sarhad Arisdakessian, Mario Chahoud, Osama Wehbi, Mohamad Ajaj, Azzam Mourad, Hadi Otrok, Omar Abdel Wahab, Rabeb Mizouni, Jamal Bentahar, Chamseddine Talhi, Zbigniew Dziong, Ernesto Damiani, Mohsen Guizani

To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.

Business Ethics Cultural Vocal Bursts Intensity Prediction

A Survey on Explainable Artificial Intelligence for Cybersecurity

no code implementations • 7 Mar 2023 • Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab, Rabeb Mizouni, Alyssa Song, Robin Cohen, Hadi Otrok, Azzam Mourad

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 Explainable Artificial Intelligence (XAI)

FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices

no code implementations • 31 Oct 2022 • Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab, Hadi Otrok, Safa Otoum, Azzam Mourad, Mohsen Guizani

Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices.

Federated Learning Privacy Preserving

Cloud computing as a platform for monetizing data services: A two-sided game business model

no code implementations • 26 Apr 2021 • Ahmed Saleh Bataineh, Jamal Bentahar, Rabeb Mizouni, Omar Abdel Wahab, Gaith Rjoub, May El Barachi

The players of the game are the big data service providers, cloud computing platform, and cloud users.

Cloud Computing

A two-level solution to fight against dishonest opinions in recommendation-based trust systems

no code implementations • 9 Jun 2020 • Omar Abdel Wahab, Jamal Bentahar, Robin Cohen, Hadi Otrok, Azzam Mourad

In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels.

Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems

no code implementations • 23 May 2019 • Adel Abusitta, Esma Aïmeur, Omar Abdel Wahab

In this paper, we propose a new framework for mitigating biases in machine learning systems.

BIG-bench Machine Learning