24 citations · 29 across the 3 of their papers we have counts for
3 papers
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research
Teresa Salazar, Helder Araújo, Alberto Cano +1
Group fairness in machine learning is an important area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or…
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning
Teresa Salazar, João Gama, Helder Araújo +1
In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-maki…
FAIR-FATE: Fair Federated Learning with Momentum
Teresa Salazar, Miguel Fernandes, Helder Araujo +1
While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underex…