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 …

Matrix computations are a fundamental building block of the edge computing systems, with a
major recent uptick in demand due to their use in AI/ML training and inference procedures …

Straggler nodes are well-known bottlenecks of distributed matrix computations which induce
reductions in computation/communication speeds. A common strategy for mitigating such …

This paper considers the problem of outsourcing the multiplication of two private and sparse
matrices to untrusted workers. Secret sharing schemes can be used to tolerate stragglers …

This work investigates the design of sparse secret sharing schemes that encode a sparse
private matrix into sparse shares. This investigation is motivated by distributed computing …

In this paper, we propose an over-the-air (OTA)-based approach for distributed matrix-vector
multiplications in the context of distributed machine learning (DML). Thanks to OTA …

We consider the problem of maintaining sparsity in private distributed storage of confidential
machine learning data. In many applications, eg, face recognition, the data used in machine …

Existing approaches to distributed matrix computations involve allocating coded
combinations of submatrices to worker nodes, to build resilience to stragglers and/or …

In this paper, we propose a sparse matrix multiplication in a decentralized setting, where a
set of worker nodes wishes to compute a task collaboratively over a logical ring. We …

In this thesis, we explore the problem of multi-user linearly-decomposable distributed
computing, where N servers help compute the desired functions (jobs) of K users, and where …