We present a cutting-edge, novel semi-supervised federated learning approach that incorporates a Deep Reinforcement Learning (DRL) agent for real-time defense ...

DRL-FedUSS is designed explicitly for. Label-at-Client scenarios to accelerate the training convergence when clients hold a scarcity of labeled and an abundance ...

In this paper, we propose a novel approach using federated semi-supervised learning for network traffic classification. In our approach, the federated servers ...

This work proposes the DRL-FedUSS framework, which stands for DRL-based Federated Uncertainty-guided Semi-Supervised learning, a cutting-edge, ...

The DRL-FedUSS framework integrates a DRL agent that intelligently selects the most informative samples with a real-time adaptive threshold for data annotation.

In this article, we propose a novel FL-empowered semi-supervised active learning (FL-SSAL) security orchestration framework for the Label-at-Client scenario.

This paper proposes a federated learning-based model incorporating the ZSM architecture for network automation and contains the simulations and results of ...

Missing: Uncertainty- guided Semi- Traffic Selection Threshold Determination ZSM.

Dec 5, 2023 · DRL-based Federated Uncertainty-guided Semi-Supervised Learning for Network Traffic Selection and Threshold Determination in ZSM. Abdullatif ...

DRL-based Federated Uncertainty-guided Semi-Supervised Learning for Network Traffic Selection and Threshold Determination in ZSM. Albaseer, Abdullatif ...