This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

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 …

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 has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Federated Learning (FL) is a privacy-enhancing technology for distributed ML. By training
models locally and aggregating updates-a federation learns together, while bypassing …

摘要联邦学习是由多个客户端协作开展模型训练的一种分布式机器学习解决方案.
在联邦学习架构下, 公平性被赋予了更加丰富的内涵: 一方面, 联邦学习中不同参与者对模型训练 …

Learning fair and transferable representations of users that can be used for a wide spectrum
of downstream tasks (specifically, machine learning models) has great potential in fairness …

Generative AI, exemplified by models like transformers, has opened up new possibilities in
various domains but also raised concerns about fairness, transparency and reliability …