A review on fairness in machine learning
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …
Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Fairness without demographics through adversarially reweighted learning
Much of the previous machine learning (ML) fairness literature assumes that protected
features such as race and sex are present in the dataset, and relies upon them to mitigate …
features such as race and sex are present in the dataset, and relies upon them to mitigate …
Differential privacy has disparate impact on model accuracy
E Bagdasaryan, O Poursaeed… - Advances in neural …, 2019 - proceedings.neurips.cc
Differential privacy (DP) is a popular mechanism for training machine learning models with
bounded leakage about the presence of specific points in the training data. The cost of …
bounded leakage about the presence of specific points in the training data. The cost of …
Privacy and fairness in Federated learning: on the perspective of Tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
Collaborative fairness in federated learning
In current deep learning paradigms, local training or the Standalone framework tends to
result in overfitting and thus low utility. This problem can be addressed by Distributed or …
result in overfitting and thus low utility. This problem can be addressed by Distributed or …
Outsider oversight: Designing a third party audit ecosystem for ai governance
Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …
developers and users of algorithmic systems accountable. But existing algorithmic …
What we can't measure, we can't understand: Challenges to demographic data procurement in the pursuit of fairness
As calls for fair and unbiased algorithmic systems increase, so too does the number of
individuals working on algorithmic fairness in industry. However, these practitioners often do …
individuals working on algorithmic fairness in industry. However, these practitioners often do …
Towards fair and privacy-preserving federated deep models
The current standalone deep learning framework tends to result in overfitting and low utility.
This problem can be addressed by either a centralized framework that deploys a central …
This problem can be addressed by either a centralized framework that deploys a central …