Dubbed “the successor to the mobile Internet,” the concept of the Metaverse has grown in
popularity. While there exist lite versions of the Metaverse today, they are still far from …

Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

We consider a scenario involving computations over a massive dataset stored distributedly
across multiple workers, which is at the core of distributed learning algorithms. We propose …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

Matrix multiplication is one of the key operations in various engineering applications.
Outsourcing large-scale matrix multiplication tasks to multiple distributed servers or cloud is …

We consider the problem of secure distributed matrix multiplication (SDMM) in which a user
wishes to compute the product of two matrices with the assistance of honest but curious …

We present two novel federated learning (FL) schemes that mitigate the effect of straggling
devices by introducing redundancy on the devices' data across the network. Compared to …

Large matrix multiplications are central to large-scale machine learning applications. These
operations are often carried out on a distributed computing platform with a master server and …

In distributed matrix multiplication, a common scenario is to assign each worker a fraction of
the multiplication task, by partitioning the input matrices into smaller submatrices. In …

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains
artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL …