141 citations · 308 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
ModularFed: Leveraging Modularity in Federated Learning Frameworks
Mohamad Arafeh, Hadi Otrok, Hakima Ould-Slimane +3
Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards…
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…
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing
Anahita Mazloomi, Hani Sami, Jamal Bentahar +2
Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by…