Federated Training Applied to Left Ventricle Segmentation

• Vinicios B. da Silva UFG
• Renan R. de Oliveira UFG / IFG
• Antonio Oliveira-Jr UFG / Fraunhofer Portugal AICOS
• Ronaldo M. da Costa UFG

DOI: https://doi.org/10.5753/erigo.2023.237317

Abstract

The sensitive nature of medical data is a challenge for the use of centralized Machine Learning models. In contrast to traditional ML, Federated Learning allows models to be trained across institutions without sharing data. Therefore, this article presents a comparative analysis of the use of a traditional ML model for the segmentation of medical images compared to the FL paradigm, highlighting its benefits in the development of collaborative models.

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