10 citations · 26 across the 5 of their papers we have counts for
7 papers
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou +1
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted…
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
Vaikkunth Mugunthan, Pawan Goyal, Lalana Kagal
Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label i…
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal +1
Federated Generative Adversarial Network (FedGAN) is a communication-efficient approach to train a GAN across distributed clients without clients having to share their sensitive tr…
Prior-Independent Auctions for the Demand Side of Federated Learning
Andreas Haupt, Vaikkunth Mugunthan
Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. While largely decentra…
DPD-InfoGAN: Differentially Private Distributed InfoGAN
Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal +1
Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN ha…
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
Vaikkunth Mugunthan, Ravi Rahman, Lalana Kagal
Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents w…