Investigating the Predictive Reproducibility of Federated Graph Neural
Networks using Medical Datasets
- URL: http://arxiv.org/abs/2209.06032v1
- Date: Tue, 13 Sep 2022 14:32:03 GMT
- Title: Investigating the Predictive Reproducibility of Federated Graph Neural
Networks using Medical Datasets
- Authors: Mehmet Yigit Balik, Arwa Rekik and Islem Rekik
- Abstract summary: We present the first work investigating the application of federated GNN models with application to classifying medical imaging and brain connectivity datasets.
We showed that federated learning boosts both the accuracy and accuracy of GNN models in such medical learning tasks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Graph neural networks (GNNs) have achieved extraordinary enhancements in
various areas including the fields medical imaging and network neuroscience
where they displayed a high accuracy in diagnosing challenging neurological
disorders such as autism. In the face of medical data scarcity and
high-privacy, training such data-hungry models remains challenging. Federated
learning brings an efficient solution to this issue by allowing to train models
on multiple datasets, collected independently by different hospitals, in fully
data-preserving manner. Although both state-of-the-art GNNs and federated
learning techniques focus on boosting classification accuracy, they overlook a
critical unsolved problem: investigating the reproducibility of the most
discriminative biomarkers (i.e., features) selected by the GNN models within a
federated learning paradigm. Quantifying the reproducibility of a predictive
medical model against perturbations of training and testing data distributions
presents one of the biggest hurdles to overcome in developing translational
clinical applications. To the best of our knowledge, this presents the first
work investigating the reproducibility of federated GNN models with application
to classifying medical imaging and brain connectivity datasets. We evaluated
our framework using various GNN models trained on medical imaging and
connectomic datasets. More importantly, we showed that federated learning
boosts both the accuracy and reproducibility of GNN models in such medical
learning tasks. Our source code is available at
https://github.com/basiralab/reproducibleFedGNN.
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