20 citations · 35 across the 4 of their papers we have counts for
6 papers
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
Pranay Sharma, Kaidi Xu, Sijia Liu +3
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations. The pro…
Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction
Prashant Khanduri, Pranay Sharma, Swatantra Kafle +3
In this work, we propose a distributed algorithm for stochastic non-convex optimization. We consider a worker-server architecture where a set of worker nodes (WNs) in collabora…
Byzantine Resilient Non-Convex SVRG with Distributed Batch Gradient Computations
Prashant Khanduri, Saikiran Bulusu, Pranay Sharma +1
In this work, we consider the distributed stochastic optimization problem of minimizing a non-convex function in an adversarial sett…
Parallel Restarted SPIDER -- Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
Pranay Sharma, Swatantra Kafle, Prashant Khanduri +3
In this paper, we propose a distributed algorithm for stochastic smooth, non-convex optimization. We assume a worker-server architecture where nodes, each having (potential…
Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory
Kush R. Varshney, Prashant Khanduri, Pranay Sharma +2
As artificial intelligence is increasingly affecting all parts of society and life, there is growing recognition that human interpretability of machine learning models is important…