14 citations · 44 across the 19 of their papers we have counts for
7 papers · 1 filter
Noise-Robust and Resource-Efficient ADMM-based Federated Learning
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To ad…
Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning
Vinay Chakravarthi Gogineni, Esmaeil S. Nadimi
Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples in an already trained machine l…
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicio…
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…