A survey on group fairness in federated learning: challenges, taxonomy of solutions and directions for future research
University of Coimbra · Institute for Systems Engineering and Computers · +1 more institution
Abstract
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 gender. Federated learning, a decentralized approach to training machine learning models across multiple clients, amplifies the need for fairness methodologies due to its inherent heterogeneous data distributions that can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 48 research works specifically dedicated to addressing this issue. However, no comprehensive survey has specifically focused on group fairness in Federated Learning. In this work, we analyze the key…
Citation impact
- FWCI
- 51.82
- Percentile
- 99%
- References
- 101
Authors
4Topics & keywords
- Taxonomy (biology)
- Key (lock)
- Federated learning
- Intersection (aeronautics)
- Identification (biology)
- Gender equality