In the era of artificial intelligence and big data, the demand for data processing has surged,
leading to larger datasets and computation capability. Distributed machine learning (DML) …

Metaverse is an immersive and photorealistic shared virtual world that requires efficient
rendering and processing of millions of virtual objects and scenes. This leads to the …

In this paper, a general framework for linear secure distributed matrix multiplication (SDMM)
is introduced. The model allows for a neat treatment of straggling and Byzantine servers via …

Federated learning (FL) is a collaborative machine-learning (ML) framework particularly
suited for ML models requiring numerous training samples, such as Convolutional Neural …

Recently, edge computing has demonstrated increasing potential to provide low-latency
computing services. Coded edge computing can not only make full use of the resources of …

In this paper, we consider coded computation for matrix multiplication tasks in distributed
computing to mitigate straggler effects. We assume that the stragglers' computation results …

This letter addresses the challenge of multiplying large real-numbered matrices and in on-
device edge computing environments with a master node and worker nodes. To ensure …

This paper considers a distributed computing framework, where the task of T matrix-vector
products is distributed among n worker machines. External adversaries have access to a …

Distributed computing has emerged as a promising solution for accelerating machine
learning training processes on large-scale datasets by leveraging the parallel processing …

In a large-scale distributed machine learning system, coded computing has attracted wide-
spread attention since it can effectively alleviate the impact of stragglers. However, several …