15 citations · 44 across the 11 of their papers we have counts for
6 papers · 1 filter
Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties
Reza Mirzaeifard, Naveen K. D. Venkategowda, Vinay Chakravarthi Gogineni +1
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute d…
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization
Reza Mirzaeifard, Naveen K. D. Venkategowda, Alexander Jung +1
This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provi…
Distributed Filtering Design with Enhanced Resilience to Coordinated Byzantine Attacks
Ashkan Moradi, Vinay Chakravarthi Gogineni, Naveen K. D. Venkategowda +1
This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF) wherein network agents employ partial sharing of state parameters. We characterize the perform…
Personalized Graph Federated Learning with Differential Privacy
Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner +2
This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device…
Private Networked Federated Learning for Nonsmooth Objectives
François Gauthier, Cristiano Gratton, Naveen K. D. Venkategowda +1
This paper develops a networked federated learning algorithm to solve nonsmooth objective functions. To guarantee the confidentiality of the participants with respect to each other…
Asynchronous Online Federated Learning with Reduced Communication Requirements
Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner +2
Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers…