With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Deep learning's widespread adoption in various fields has made distributed training across
multiple computing nodes essential. However, frequent communication between nodes can …

Smart cities have seen a growing interest among governments, researchers, and industries.
Smart cities use digital technologies to enhance the quality of life for residents while …

This systematic review investigates compression algorithms for three-dimensional content,
focusing on recent advancements. It categorizes the methodologies into traditional, learning …

In the hyperspectral image (HSI) classification tasks, various deep learning models have
achieved remarkable success. However, most deep learning models are compute-intensive …

Traditional machine learning involves collecting data from clients to a central server, where
data may not be willingly shared by the clients. In contrast, federated learning (FL) trains a …

Driven by the ever-growing volume and decentralized nature of data, coupled with the
escalating size of modern models, distributed deep learning (DDL) has been entrenched as …

As machine learning data sizes continue to rise, this work offers a unique solution to improve
computing efficiency by means of data compression. This starts by going over the problem's …