33 citations · 139 across the 42 of their papers we have counts for
49 papers
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 computation with gradient coding
Kyungrak Son, Aditya Ramamoorthy
Polynomial based approaches, such as the Mat-Dot and entangled polynomial codes (EPC) have been used extensively within coded matrix computations to obtain schemes with good recove…
Communication complexity of entanglement assisted multi-party computation
Ruoyu Meng, Aditya Ramamoorthy
We consider a quantum and classical version multi-party function computation problem with players, where players need to communicate appropriate information to pl…
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…
A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation
Anindya Bijoy Das, Aditya Ramamoorthy
The overall execution time of distributed matrix computations is often dominated by slow worker nodes (stragglers) within the clusters. Recently, different coding techniques have b…