Trust Driven On-Demand Scheme for Client Deployment in Federated Learning

no code implementations • 1 May 2024 • Mario Chahoud, Azzam Mourad, Hadi Otrok, Jamal Bentahar, Mohsen Guizani

In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture.

Federated Learning

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

no code implementations • 18 Apr 2023 • Hani Sami, Ahmad Hammoud, Mouhamad Arafeh, Mohamad Wazzeh, Sarhad Arisdakessian, Mario Chahoud, Osama Wehbi, Mohamad Ajaj, Azzam Mourad, Hadi Otrok, Omar Abdel Wahab, Rabeb Mizouni, Jamal Bentahar, Chamseddine Talhi, Zbigniew Dziong, Ernesto Damiani, Mohsen Guizani

To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions.

Business Ethics Cultural Vocal Bursts Intensity Prediction

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

no code implementations • 5 Nov 2022 • Mario Chahoud, Hani Sami, Azzam Mourad, Safa Otoum, Hadi Otrok, Jamal Bentahar, Mohsen Guizani

In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process.

Federated Learning