As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Does Federated Learning (FL) work when both uplink and downlink communications have
errors? How much communication noise can FL handle and what is its impact on the …

Federated learning (FL) that enables edge devices to collaboratively learn a shared model
while keeping their training data locally has received great attention recently and can protect …

Gradient compression (eg, gradient quantization and gradient sparsification) is a core
technique in reducing communication costs in distributed learning systems. The recent trend …

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 …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …

Nowadays, the training data and neural network models are getting increasingly large. The
training time of deep learning will become unbearably long on a single machine. To reduce …

Federated learning (FL) is a machine learning framework, where multiple distributed edge
Internet of Things (IoT) devices collaboratively train a model under the orchestration of a …