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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Arisdakessian, S., Wehbi, O., Abdul Wahab, O., Mourad, A., & Otrok, H. (2025). Towards Vox Populi in Federated Learning: A Fair and Inclusive Client Selection Framework. IEEE Transactions on Artificial Intelligence, 1, 1-15. External link
Arisdakessian, S., Abdul Wahab, O., Wehbi, O., Mourad, A., & Otrok, H. (2025, July). Improving Class-Level Fairness Under Non-IID Data Distributions in Federated Learning [Paper]. 45th International Conference on Distributed Computing Systems Workshops (ICDCSW 2025), Glasgow, United Kingdom. External link
Arisdakessian, S., Wehbi, O., Abdul Wahab, O., Mourad, A., Otrok, H., & Guizani, M. (2025). A Two-Level Dirichlet Framework for Heterogeneous Federated Network. IEEE Transactions on Network Science and Engineering. External link
Arisdakessian, S., Abdul Wahab, O., Wehbi, O., Mourad, A., & Otrok, H. (2024, September). Detecting Free-Riders in Federated Learning Using an Ensemble of Similarity Distance Metrics [Paper]. 4th Intelligent Cybersecurity Conference (ICSC 2024), Valencia, Spain. External link
Arisdakessian, S., Wahab, O. A., Wehbi, O., Mourad, A., & Otrok, H. (2024, July). Trustworthy Hierarchical Federated Learning for Digital Healthcare [Paper]. IEEE Annual Congress on Artificial Intelligence of Things (AIoT 2024), Melbourne, Australia. External link
Sami, H., Hammoud, A., Arafeh, M., Wazzeh, M., Arisdakessian, S., Chahoud, M., Wehbi, O., Ajaj, M., Mourad, A., Otrok, H., Wahab, O. A., Mizouni, R., Bentahar, J., Talhi, C., Dziong, Z., Damiani, E., & Guizani, M. (2024). The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions. IEEE Communications Surveys and Tutorials, 3392642 (49 pages). External link
Wehbi, O., Arisdakessian, S., Guizani, M., Wahab, O. A., Mourad, A., Otrok, H., Khzaimi, H. A., & Ouni, B. (2024). Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities. IEEE Internet of Things Journal, 1-1. External link
Wehbi, O., Arisdakessian, S., Abdul Wahab, O., Otrok, H., Otoum, S., Mourad, A., & Guizani, M. (2023). FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices. IEEE Internet of Things Journal, 15 pages. External link
Wehbi, O., Abdul Wahab, O., Mourad, A., Otrok, H., Alkhzaimi, H., & Guizani, M. (2023, June). Towards Mutual Trust-Based Matching For Federated Learning Client Selection [Paper]. 2023 International Wireless Communications and Mobile Computing (IWCMC 2023), Marrakesh, Morocco. External link
