Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models

HIGHLIGHTS

• What: Each dataset represents a different hospital, which the authors will use for this analysis as a isolated silo and the number of patients in each dataset is reported in the last row of the Tables 1 and 2. By performing cross-validation twice, the authors aimed to generate a more robust estimation of the model`s performance metrics by averaging the results over two separate runs, each partitioning the data differently. The work demonstrated the performance of distributed models using real-world data by comparing their performance with that of local models, which are trained with . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.