54 citations · 54 across the 6 of their papers we have counts for
6 papers
SCRUB-FL: Sanitizing and Cleansing Representations via Unlearning of Backdoors
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +2
Federated Learning (FL) enables collaborative model training without sharing raw data, making it a promising paradigm for privacy-sensitive applications. However, its decentralized…
FL-PBM: Pre-Training Backdoor Mitigation for Federated Learning
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3
Backdoor attacks pose a significant threat to the integrity and reliability of Artificial Intelligence (AI) models, enabling adversaries to manipulate model behavior by injecting p…
Mitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab +3
Federated Learning (FL) is witnessing wider adoption due to its ability to benefit from large amounts of scattered data while preserving privacy. However, despite its advantages, f…
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…
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…