FedCMC: a federated learning model with contribution fairness based on multi-center core data extraction for assessing the myometrial invasion status of endometrial cancer.

ABSTRACT:

Background

Multi-center Federated Learning (FL) has played a significant role in disease prediction, offering a feasible solution to the challenges of cross-institutional collaboration. However, the fairness issues inherent in traditional FL frameworks have limited their further development in the medical field.

Methods

We propose a Contribution Fairness Federated Learning model based on Multi-center Core Data Extraction (FedCMC). This model accurately assesses the actual contributions of each center from both data and model perspectives using two fairness indicators: data information richness and model quality. In the data contribution assessment phase, we innovatively design a Multi-center Core Data Extraction Module (MCDEM). This module extracts representative core datase

SUBMITTER: Li Y 

PROVIDER: S-EPMC12454072 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature