1 citations · 2 across the 12 of their papers we have counts for
13 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…
From Connectivity to Multi-Orbit Intelligence: Space-Based Data Center Architectures for 6G and Beyond
Shimaa Naser, Maryam Tariq, Raneem Abdel-Rahim +6
Direct handset-to-satellite (DHTS) communication is emerging as a core capability of 6G non-terrestrial networks, enabling standard devices to directly access low Earth orbit (LEO)…
End-to-End Framework Integrating Generative AI and Deep Reinforcement Learning for Autonomous Ultrasound Scanning
Hanae Elmekki, Amanda Spilkin, Ehsan Zakeri +11
Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time const…
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging
Hanae Elmekki, Saidul Islam, Ahmed Alagha +12
Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability…
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning
Hanae Elmekki, Ahmed Alagha, Hani Sami +11
Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider…
A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions
Gordon Owusu Boateng, Hani Sami, Ahmed Alagha +10
The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands…
On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
Mario Chahoud, Hani Sami, Azzam Mourad +3
In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding cl…
CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication
Mohamad Wazzeh, Mohamad Arafeh, Hani Sami +4
In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) an…
Federated Learning and Evolutionary Game Model for Fog Federation Formation
Zyad Yasser, Ahmad Hammoud, Azzam Mourad +3
In this paper, we tackle the network delays in the Internet of Things (IoT) for an enhanced QoS through a stable and optimized federated fog computing infrastructure. Network delay…
Trust Driven On-Demand Scheme for Client Deployment in Federated Learning
Mario Chahoud, Azzam Mourad, Hadi Otrok +2
Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for ea…
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…