Experimenting Federated AI Models for Hematological Diseases

Federated learning is an Artificial Intelligence framework that allows to train machine learning models in a distributed way, avoiding the sharing of sensitive data. This method is crucial, especially in healthcare applications, where patient privacy is an enormous concern. This study aims to demonstrate the effectiveness of federated learning in addressing real use-cases. In addition, the platform developed by the GenoMed4All consortium was tested against computational simulations. Two real use-cases were considered: a survival estimation task on a Myelodysplastic Syndrome cohort, and a classification task on a scarce Sickle Cell Disease dataset. Both cohorts were distributed in a federated learning scenario with 3 clients and a common test set. Federated learning came out to be crucial in improving the performance of local survival models, especially for nodes with the lower number of samples, which most benefit from federated aggregation. Despite the limited number of patients for the classification task, federated learning consistently improved model performance across multiple metrics beyond the F1 score, including comparisons between different sample distributions among the three clients. These results confirmed the effectiveness of federated learning in healthcare applications, especially for scarce datasets, for which this technique can represent a viable solution. Moreover, the results of the GenoMed4All platform are completely in agreement with the computer simulations, proving the reliability of the developed platform.