238 citations · 552 across the 13 of their papers we have counts for
13 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…
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…
Distributed Estimation in the Presence of Strategic Data Sources
Kewei Chen, Donya Ghavidel, Vijay Gupta +1
Distributed estimation that recruits potentially large groups of humans to collect data about a phenomenon of interest has emerged as a paradigm applicable to a broad range of dete…