141 citations · 308 across the 23 of their papers we have counts for
5 papers · 1 filter
Blockchain-based Crowdsourced Deep Reinforcement Learning as a Service
Ahmed Alagha, Hadi Otrok, Shakti Singh +2
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its…
Blockchain-assisted Demonstration Cloning for Multi-Agent Deep Reinforcement Learning
Ahmed Alagha, Jamal Bentahar, Hadi Otrok +2
Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes…
Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
Ahmed Alagha, Rabeb Mizouni, Shakti Singh +2
Target localization is a critical task in sensitive applications, where multiple sensing agents communicate and collaborate to identify the target location based on sensor readings…
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…
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…