1 citations · 2 across the 4 of their papers we have counts for
4 papers
ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme
Mario Chahoud, Hani Sami, Azzam Mourad +4
In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of…
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…
Reward Shaping Using Convolutional Neural Network
Hani Sami, Hadi Otrok, Jamal Bentahar +2
In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN…
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
Omar Abdel Wahab, Jamal Bentahar, Robin Cohen +2
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in…