Powerful computer clusters are used nowadays to train complex deep neural networks
(DNN) on large datasets. Distributed training increasingly becomes communication bound …

The recent many-fold increase in the size of deep neural networks makes efficient
distributed training challenging. Many proposals exploit the compressibility of the gradients …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private datasets owned by non-trusting entities. FL has seen successful …

Training deep neural networks on large datasets containing high-dimensional data requires
a large amount of computation. A solution to this problem is data-parallel distributed training …

Sparse tensors appear frequently in federated deep learning, either as a direct artifact of the
deep neural network's gradients, or as a result of an explicit sparsification process. Existing …

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to
collaboratively train a global machine learning model without centralizing data and with …

In contrast to training traditional machine learning (ML) models in data centers, federated
learning (FL) trains ML models over local datasets contained on resource-constrained …

We consider federated learning in a wireless edge network, where multiple power-limited
mobile devices collaboratively train a global model, using their local data with the assistance …

In distributed machine learning training, bulk synchronous parallel (BSP) and asynchronous
parallel (ASP) are two main synchronization methods to help achieve gradient aggregation …

Day trading has been gaining attention from prospective investors over the past decades,
even more so in the last decade due to a plethora of factors such as instantaneous …