A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908533768847360 |
|---|---|
| author | Salazar, Teresa Araújo, Helder Cano, Alberto Abreu, Pedro Henriques |
| author_facet | Salazar, Teresa Araújo, Helder Cano, Alberto Abreu, Pedro Henriques |
| contents | 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 challenges of this topic, propose practices for its identification and benchmarking, and create a novel taxonomy based on criteria such as data partitioning, location, and strategy. Furthermore, we analyze broader concerns, review how different approaches handle the complexities of various sensitive attributes, examine common datasets and applications, and discuss the ethical, legal, and policy implications of group fairness in FL. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research Salazar, Teresa Araújo, Helder Cano, Alberto Abreu, Pedro Henriques Machine Learning Artificial Intelligence Computers and Society 68T01 I.2.6; I.5.1; K.4.1 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 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 challenges of this topic, propose practices for its identification and benchmarking, and create a novel taxonomy based on criteria such as data partitioning, location, and strategy. Furthermore, we analyze broader concerns, review how different approaches handle the complexities of various sensitive attributes, examine common datasets and applications, and discuss the ethical, legal, and policy implications of group fairness in FL. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated systems. |
| title | A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research |
| topic | Machine Learning Artificial Intelligence Computers and Society 68T01 I.2.6; I.5.1; K.4.1 |
| url | https://arxiv.org/abs/2410.03855 |