Search Results for author:
Found 9 papers, 4 papers with code
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
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 information is owned by a single data owner.
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Our approach generates metadata at the aggregator using the models received from clients and retrains the federated model to achieve bias-free results for image synthesis.
Investigating Bias in Image Classification using Model Explanations
We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive attributes for fairness calculations.
DPD-InfoGAN: Differentially Private Distributed InfoGAN
The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced.
BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning
When run on the public Ethereum blockchain, BlockFLow uses the results from the audit to reward parties with cryptocurrency based on the quality of their contribution.
PrivacyFL: A simulator for privacy-preserving and secure federated learning
In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages.
Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence
New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data.
Human-Computer Interaction Computers and Society
Explaining Explanations: An Overview of Interpretability of Machine Learning
There has recently been a surge of work in explanatory artificial intelligence (XAI).
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+1
Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models
Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment.
