14 citations · 27 across the 6 of their papers we have counts for
8 papers
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…
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…
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…
Model Approximation Using Cascade of Tree Decompositions
Navid Tafaghodi Khajavi, Anthony Kuh
In this paper, we present a general, multistage framework for graphical model approximation using a cascade of models such as trees. In particular, we look at the problem of covari…
How Consumer Empathy Assist Power Grid in Demand Response
Mojtaba Abolfazli, June Zhang, Anthony Kuh
This paper investigates interaction among residential electricity users and utility company in a distribution network with the capability of two-way communication provided by smart…
The Goodness of Covariance Selection Problem from AUC Bounds
Navid Tafaghodi Khajavi, Anthony Kuh
We conduct a study of graphical models and discuss the quality of model selection approximation by formulating the problem as a detection problem and examining the area under the c…
The Quality of the Covariance Selection Through Detection Problem and AUC Bounds
Navid Tafaghodi Khajavi, Anthony Kuh
We consider the problem of quantifying the quality of a model selection problem for a graphical model. We discuss this by formulating the problem as a detection problem. Model sele…