With the development of edge devices and cloud computing, the question of how to
accomplish machine learning and optimization tasks in a privacy-preserving and secure way …

While fairness-aware machine learning algorithms have been receiving increasing attention,
the focus has been on centralized machine learning, leaving decentralized methods …

In recent years, multi-objective optimization (MOO) emerges as a foundational problem
underpinning many multi-agent multi-task learning applications. However, existing …

The issue of group fairness in machine learning models, where certain sub-populations or
groups are favored over others, has been recognized for some time. While many mitigation …

As a new distributed machine learning (ML) approach, federated learning (FL) shows great
potential to preserve data privacy by enabling distributed data owners to collaboratively …

In the evolving field of machine learning, ensuring fairness has become a critical concern,
prompting the development of algorithms designed to mitigate discriminatory outcomes in …

In the evolving field of machine learning, ensuring fairness has become a critical concern,
prompting the development of algorithms designed to mitigate bias in decision-making …

While fairness-aware machine learning algorithms have been receiving increasing attention,
the focus has been on centralized machine learning, leaving decentralized methods …