Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations. (arXiv:2308.04331v1 [cs.IT])

Existing approaches to distributed matrix computations involve allocating
coded combinations of submatrices to worker nodes, to build resilience to
stragglers and/or enhance privacy. In this study, we consider the challenge of
preserving input sparsity in such approaches to retain the associated
computational efficiency enhancements. First, we find a lower bound on the
weight of coding, i.e., the number of submatrices to be combined to obtain
coded submatrices to provide the resilience to the maximum possible number of
stragglers (for given …

build challenge computational distributed efficiency find input matrix nodes privacy resilience retain study worker