activity
20182021
most citedSTEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

20 citations · 57 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2021★ 20 cited

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…

stat.ML2020★ 4 cited

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…

math.OC2020★ 1 cited

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…

math.OC2020

On Distributed Online Convex Optimization with Sublinear Dynamic Regret and Fit

Pranay Sharma, Prashant Khanduri, Lixin Shen +2

In this work, we consider a distributed online convex optimization problem, with time-varying (potentially adversarial) constraints. A set of nodes, jointly aim to minimize a globa…

math.OC2019★ 10 cited

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…

math.OC2019★ 13 cited

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…

stat.ML2018★ 9 cited

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…