Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

Distributed training techniques have been widely deployed in large-scale deep models
training on dense-GPU clusters. However, on public cloud clusters, due to the moderate …

Increasingly rich data sets and complicated models make distributed machine learning more
and more important. However, the cost of extensive and frequent parameter …

Recent trends in high-performance computing and deep learning have led to the
proliferation of studies on large-scale deep neural network training. However, the frequent …

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

Scalable and efficient distributed learning is one of the main driving forces behind the recent
rapid advancement of machine learning and artificial intelligence. One prominent feature of …

Federated learning enables participants to collaborate on model training without directly
exchanging raw data. Existing federated learning methods often follow the parameter server …

With the rapid development of big data and the Internet of Things (IoT), traffic data from an
Intelligent Transportation System (ITS) is becoming more and more accessible. To …

As Deep Learning (DL) models grow larger and more complex, training jobs are
increasingly distributed across multiple Computing Units (CU) such as GPUs and TPUs …