Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

The high computational and memory requirements of generative large language models
(LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary …

Due to the rising concerns on privacy protection, how to build machine learning (ML) models
over different data sources with security guarantees is gaining more popularity. Vertical …

Transformer models have achieved state-of-the-art performance on various domains of
applications and gradually becomes the foundations of the advanced large deep learning …

Embedding models have been an effective learning paradigm for high-dimensional data.
However, one open issue of embedding models is that their representations (latent factors) …

With the fast growth of parameter size, it becomes increasingly challenging to deploy large
generative models as they typically require large GPU memory consumption and massive …

Distributed machine learning on edges is widely used in intelligent transportation, smart
home, industrial manufacturing, and underground pipe network monitoring to achieve low …

Communication scheduling has been shown to be effective in accelerating distributed
training, which enables all-reduce communications to be overlapped with backpropagation …

The increasing size of both deep learning models and training data necessitates the ability
to scale out model training through pipeline-parallel training, which combines pipelined …

Embedding models have been recognized as an effective learning paradigm for high-
dimensional data. However, a major embedding model training obstacle is that updating …