.png){kind=icon}
Return
Fairness-Driven Private Collaborative Machine Learning
.png){kind=icon}
OA
AI
DOI：10.1145/3639368.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The performance of machine learning algorithms can be considerably improved when trained over larger datasets. In many domains, such as medicine and finance, larger datasets can be obtained if several parties, each having access to limited amounts of data, collaborate and share their data. However, such data sharing introduces significant privacy challenges. While multiple recent studies have investigated methods for private collaborative machine learning, the fairness of such collaborative algorithms has been overlooked. In this work, we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. An extensive evaluation of the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.
Keywords:
Privacy
algorithmic fairness
collaborative machine learning
federated learning
secure multi-party computation
Journal
IF:
6.6
Papers:
1.5K
Citations:
6.2K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveEvolution and development of the tetrapod auditory system: an organ of Corti‐centric perspectiveOAAI
OAAIShareSaveA clinical study on the surgical treatment of simple multiple rib fractures in older adult patientsOAAI
OAAIShareSave
View more