In this paper we introduce federated learning (FL) based resource allocation (RA) for
wireless communication networks, where users cooperatively train a RA policy in a …

In cellular networks, resource allocation is usually performed in a centralized way, which
brings huge computation complexity to the base station (BS) and high transmission …

Decentralized deep learning (DL) based resource allocation (RA) in communication
networks guarantees scalability and higher communication bandwidth efficiency compared …

In cellular networks, resource allocation is performed in a centralized way, which brings
huge computation complexity to the base station (BS) and high transmission overhead. This …

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 …

In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …

A well-trained deep neural network (DNN) enables real-time resource allocation by learning
the relationship between a policy and its impacting parameters. When wireless systems …

We consider a many-to-one wireless architecture for federated learning at the network edge,
where multiple edge devices collaboratively train a model using local data. The unreliable …

We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …

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' …