20 citations · 27 across the 13 of their papers we have counts for
3 papers · 2 filters
Preserving Sparsity and Privacy in Straggler-Resilient Distributed Matrix Computations
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Existing approaches to distributed matrix computations involve allocating coded combinations of submatrices to worker nodes, to build resilience to stragglers and/or enhance privac…
Coded Matrix Computations for D2D-enabled Linearized Federated Learning
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible…
Distributed Matrix Computations with Low-weight Encodings
Anindya Bijoy Das, Aditya Ramamoorthy, David J. Love +1
Straggler nodes are well-known bottlenecks of distributed matrix computations which induce reductions in computation/communication speeds. A common strategy for mitigating such str…