Federated learning (FL), as an emerging distributed machine learning paradigm, allows a
mass of edge devices to collaboratively train a global model while preserving privacy. In this …

Deep neural networks (DNNs) with noisy weights, which we refer to as noisy neural
networks (NoisyNNs), arise from the training and inference of DNNs in the presence of …

We examine federated learning (FL) with over-the-air (OTA) aggregation, where mobile
users (MUs) aim to reach a consensus on a global model with the help of a parameter server …

Federated learning (FL) is recognized as a promising privacy-preserving distributed
machine learning paradigm, given its potential to enable collaborative model training among …

We demonstrate that merely analog transmissions and match filtering can realize the
function of an edge server in federated learning (FL). Therefore, a network with massively …

We consider the problem of autonomous channel access (AutoCA), where a group of
terminals tries to discover a communication strategy with an access point (AP) via a common …

Over-the-air Computation (AirComp) has attracted significant attention as an efficient way of
data fusion by inte-grating uncoded communication transmissions with computation thanks …

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

Over-the-air computing (AirComp) has recently attracted considerable attention as an
efficient method of data fusion by integrating uncoded communication transmissions with …

5G radio access network (RAN) is consuming much more energy than legacy RAN due to
the denser deployments of gNodeBs (gNBs) and higher single-gNB power consumption. In …