We investigate a data quality-aware dynamic client selection problem for multiple federated
learning (FL) services in a wireless network, where each client offers dynamic datasets for …

As deep reinforcement learning (RL) showcases its strengths in networking and systems, its
pitfalls also come to the public's attention--when trained to handle a wide range of network …

With the great success of deep learning (DL) in image classification, speech recognition,
and other fields, more and more studies have applied various neural networks (NNs) to …

This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …

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 …

In this paper, we design a new smart softwaredefined radio access network (RAN)
architecture with important properties like flexibility and traffic awareness for sixth generation …

In this paper, efficient gradient updating strategies are developed for the federated learning
when distributed clients are connected to the server via a wireless backhaul link …

The past few years have witnessed a surge of interest towards deep reinforcement learning
(DRL) in computer networks. With extraordinary ability of feature extraction, DRL has the …

The growing number of complex and heterogeneous nodes and base station applications
has required a high computational complexity to handle wireless resources. To tackle this …

Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G)
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …