15 citations · 47 across the 10 of their papers we have counts for
6 papers · 1 filter
Accelerating Distributed SGD for Linear Regression using Iterative Pre-Conditioning
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the multi-agent distributed linear least-squares problem. The system comprises multiple agents, each agent with a locally observed set of data points, and a co…
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy
Nirupam Gupta, Thinh T. Doan, Nitin H. Vaidya
This paper considers the problem of Byzantine fault-tolerance in multi-agent decentralized optimization. In this problem, each agent has a local cost function. The goal of a decent…
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)
Nirupam Gupta, Shuo Liu, Nitin H. Vaidya
This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learn…
Preserving Statistical Privacy in Distributed Optimization
Nirupam Gupta, Shripad Gade, Nikhil Chopra +1
We present a distributed optimization protocol that preserves statistical privacy of agents' local cost functions against a passive adversary that corrupts some agents in the netwo…
Resilience in Collaborative Optimization: Redundant and Independent Cost Functions
Nirupam Gupta, Nitin H. Vaidya
This report considers the problem of Byzantine fault-tolerance in multi-agent collaborative optimization. In this problem, each agent has a local cost function. The goal of a colla…
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper considers the problem of multi-agent distributed optimization. In this problem, there are multiple agents in the system, and each agent only knows its local cost functio…