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 …

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 …

Training ML models which are fair across different demographic groups is of critical
importance due to the increased integration of ML in crucial decision-making scenarios such …

How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Recently, lots of algorithms have been proposed for learning a fair classifier from
decentralized data. However, many theoretical and algorithmic questions remain open. First …

Federated learning is an increasingly popular paradigm that enables a large number of
entities to collaboratively learn better models. In this work, we study minimax group fairness …

Achieving fairness over different user groups in recommender systems is an important
problem. The majority of existing works achieve fairness through constrained optimization …

The proliferation of artificial intelligence systems and their reliance on massive datasets
have led to a renewed demand on privacy of data. Both the large data processing need and …