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 …

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 …

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 …

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 …

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 …

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 …

Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

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 …

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 …

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 …