Edge computing is a (r) evolutionary extension of traditional cloud computing. It expands
central cloud infrastructure with execution environments close to the users in terms of …

Distributed computing enables large-scale computation tasks to be processed by multiple
workers in parallel. However, the randomness of communication and computation delays …

In this work, we explore the problem of multi-user linearly-separable distributed computation,
where servers help compute the desired functions (jobs) of users, and where each desired …

In this work, we investigate the problem of multi-user linearly separable function
computation, where N servers help compute the desired functions (jobs) of K users. In this …

Distributed implementations are crucial in speeding up large scale machine learning
applications. Distributed gradient descent (GD) is widely employed to parallelize the …

Coded distributed computing (CDC) has shown great potentials to solve the unexpected
delay caused by stragglers in distributed computing. In this paper, we focus on the auction …

Coded distributed computing (CDC) has emerged as a promising approach because it
enables computation tasks to be carried out in a distributed manner while mitigating …

Emerging graph neural network (GNN) has recently attracted much attention and has been
used extensively in many real-world applications thanks to its powerful expression ability of …

As the amount of data collected for crowdsensing applications increases rapidly due to
improved sensing capabilities and the increasing number of Internet of Things (IoT) devices …

Federated Learning (FL) is an interesting strategy that enables the collaborative training of
an AI model among different data owners without revealing their private datasets. Even so …