IEEE

Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset

Abstract: Federated Learning is a distributed machine learning paradigm that enables model training across decentralized devices holding local data, thereby preserving data privacy and reducing the ...
IEEE

O-RAN-Based Digital Twin Function Virtualization for Sustainable IoV Service Response: An Asynchronous Hierarchical Reinforcement Learning Approach

Abstract: Digital Twin for Vehicular Networks (DTVN) continuously simulates and optimizes vehicle behaviors to support emerging 6G Internet-of-Vehicle (IoV) applications such as DT-assisted autonomous ...