Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

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 …

The allreduce operation is the most commonly used collective operation in distributed or
parallel applications. It aggregates data collected from distributed hosts and broadcasts the …

Data centers are getting more and more attention due the rapid increase of IoT deployments,
which may result in the implementation of smaller facilities being closer to the end users as …

As deep neural networks (DNNs) grow in complexity and size, the resultant increase in
communication overhead during distributed training has become a significant bottleneck …

Real-time analytics in Industrial Internet-of-Things (IIoT) has received remarkable attention
recently due to its capacity to prevent downtime and manage risks. However, the sensed …

By offloading partial of the aggregation computation from the logical central parameter
servers to network devices like programmable switches, In-Network Aggregation (INA) is a …

Distributed machine learning is an effective method to alleviate intensive computation costs
of training; however it suffers from network bottlenecks while gathering local results. Recent …

The industrial Internet represents a significant innovation merging Internet technologies, big
data, artificial intelligence, and advanced manufacturing, which is characterized by high …

In heterogeneous edge computing, multiple tasks often compete for limited computing
resources on the same edge server. These tasks request different edge computing services …