20 citations · 27 across the 13 of their papers we have counts for
13 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…
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning
Satyavrat Wagle, Anindya Bijoy Das, David J. Love +1
Augmenting federated learning (FL) with direct device-to-device (D2D) communications can help improve convergence speed and reduce model bias through rapid local information exchan…
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 Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Myeung Suk Oh, Anindya Bijoy Das, Seyyedali Hosseinalipour +3
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun addin…
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…
Coded sparse matrix computation schemes that leverage partial stragglers
Anindya Bijoy Das, Aditya Ramamoorthy
Distributed matrix computations over large clusters can suffer from the problem of slow or failed worker nodes (called stragglers) which can dominate the overall job execution time…
Straggler-resistant distributed matrix computation via coding theory
Aditya Ramamoorthy, Anindya Bijoy Das, Li Tang
The current BigData era routinely requires the processing of large scale data on massive distributed computing clusters. Such large scale clusters often suffer from the problem of…
Efficient and Robust Distributed Matrix Computations via Convolutional Coding
Anindya B. Das, Aditya Ramamoorthy, Namrata Vaswani
Distributed matrix computations -- matrix-matrix or matrix-vector multiplications -- are well-recognized to suffer from the problem of stragglers (slow or failed worker nodes). Muc…
Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
Anindya Bijoy Das, Aditya Ramamoorthy
Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix c…
: Codes for Coded Computation that Leverage Stragglers
Anindya B. Das, Li Tang, Aditya Ramamoorthy
In distributed computing systems, it is well recognized that worker nodes that are slow (called stragglers) tend to dominate the overall job execution time. Coded computation utili…
Facile Algebraic Representation of a Novel Quaternary Logic
Ifat Jahangir, Anindya Das, Masud Hasan
In this work, a novel quaternary algebra has been proposed that can be used to implement an arbitrary quaternary logic function in more than one systematic ways. The proposed logic…
On the Design and Analysis of Quaternary Serial and Parallel Adders
Anindya Das, Ifat Jahangir, Masud Hasan
Optimization techniques for decreasing the time and area of adder circuits have been extensively studied for years mostly in binary logic system. In this paper, we provide the nece…