15 citations · 44 across the 11 of their papers we have counts for
11 papers
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…
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions
Cristiano Gratton, Naveen K. D. Venkategowda, Reza Arablouei +1
We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions. Such learning problems are…
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner +2
Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the r…
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang +1
Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced…
On Stability and Convergence of Distributed Filters
Sayed Pouria Talebi, Stefan Werner, Vijay Gupta +1
Recent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively es…
Privacy-Preserving Distributed Zeroth-Order Optimization
C. Gratton, N. K. D. Venkategowda, R. Arablouei +1
We develop a privacy-preserving distributed algorithm to minimize a regularized empirical risk function when the first-order information is not available and data is distributed ov…