15 citations · 47 across the 10 of their papers we have counts for
6 papers · 1 filter
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning
Shuo Liu, Nirupam Gupta, Nitin Vaidya
This paper considers the problem of resilient distributed optimization and stochastic machine learning in a server-based architecture. The system comprises a server and multiple ag…
Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy
Nirupam Gupta, Thinh T. Doan, Nitin Vaidya
We consider the problem of Byzantine fault-tolerance in federated machine learning. In this problem, the system comprises multiple agents each with local data, and a trusted centra…
On Accelerating Distributed Convex Optimizations
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
This paper studies a distributed multi-agent convex optimization problem. The system comprises multiple agents in this problem, each with a set of local data points and an associat…
Asynchronous Distributed Optimization with Redundancy in Cost Functions
Shuo Liu, Nirupam Gupta, Nitin H. Vaidya
This paper considers the problem of asynchronous distributed multi-agent optimization on server-based system architecture. In this problem, each agent has a local cost, and the goa…
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot +2
This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Sto…
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
Nirupam Gupta, Nitin H. Vaidya
We consider the problem of Byzantine fault-tolerance in the peer-to-peer (P2P) distributed gradient-descent method -- a prominent algorithm for distributed optimization in a P2P sy…