In this paper, the convergence time of federated reinforcement learning (FRL) that is
deployed over a realistic wireless network is studied. In the considered model, several …

One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which
causes accuracy degradation, slow convergence, and the communication bottleneck issue …

Sažetak Deep reinforcement learning can effectively deal with resource allocation (RA) in
wireless networks. However, more complex networks can have slower learning speeds, and …

Machine learning and data-driven approaches have recently received considerable
attention as key enablers for next-generation intelligent networks. Currently, most existing …

Deep reinforcement learning can effectively address resource allocation in wireless
networks. However, its learning speed may be slower in more complex networks and a new …

Federated Learning (FL) enables large-scale machine learning without uploading the
private data of wireless devices. Due to the heterogeneity and limitation of the devices' …

Federated learning (FL) has recently received considerable attention in Internet of Things,
due to its capability of letting multiple clients collaboratively train machine learning models …

Federated Learning (FL) is widely regarded as a leading distributed machine learning
paradigm, owing to its outstanding performance in preserving privacy and conserving …

The paper considers independent reinforcement learning (IRL) for multi-agent collaborative
decision-making in the paradigm of federated learning (FL). However, FL generates …

Advances in Artificial Intelligence (AI) provide new capabilities to handle network routing
problems. However, the lack of up-to-date training data, slow convergence, and low …