FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction

Documenti analoghi

• Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
  di: Liu, Mingxuan, et al.
  Pubblicazione: (2024)
• Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
  di: Li, Siqi, et al.
  Pubblicazione: (2024)
• Toward Fair Federated Learning under Demographic Disparities and Data Imbalance
  di: Wu, Qiming, et al.
  Pubblicazione: (2025)
• Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
  di: Li, Siqi, et al.
  Pubblicazione: (2023)
• Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomes
  di: Wang, Ziwen, et al.
  Pubblicazione: (2026)