33 citations · 254 across the 68 of their papers we have counts for
9 papers · 2 filters
Timestamps for Partial Replication
Zhuolun Xiang, Nitin H. Vaidya
Maintaining causal consistency in distributed shared memory systems using vector timestamps has received a lot of attention from both theoretical and practical prospective. However…
Private Learning on Networks
Shripad Gade, Nitin H. Vaidya
Continual data collection and widespread deployment of machine learning algorithms, particularly the distributed variants, have raised new privacy challenges. In a distributed mach…
Distributed Optimization of Convex Sum of Non-Convex Functions
Shripad Gade, Nitin H. Vaidya
We present a distributed solution to optimizing a convex function composed of several non-convex functions. Each non-convex function is privately stored with an agent while the age…
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights
Shripad Gade, Nitin H. Vaidya
Availability of both massive datasets and computing resources have made machine learning and predictive analytics extremely pervasive. In this work we present a synchronous algorit…
Defending Non-Bayesian Learning against Adversarial Attacks
Lili Su, Nitin H. Vaidya
This paper addresses the problem of non-Bayesian learning over multi-agent networks, where agents repeatedly collect partially informative observations about an unknown state of th…
Efficient Timestamps for Capturing Causality
Nitin H. Vaidya, Sandeep S. Kulkarni
Consider an asynchronous system consisting of processes that communicate via message-passing. The processes communicate over a potentially {\em incomplete} communication network co…