On the Feasibility of Federated Learning for Neurodevelopmental Disorders: ASD Detection Use-Case

Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome resulting from alterations in the embryological brain pre-birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior, in addition to specific behavioral traits, deteriorating their social behavior and interaction within their community. Moreover, medical research has proved that ASD affects the facial features of its patients, making the syndrome recognizable from distinctive signs within an individual's face. Given that as a motivation behind our work, we propose a novel privacy-preserving FL model, in order to predict ASD in a certain individual based on their behavioral traits or facial features, while respecting patient data privacy, as ASD data is medical and hence sensitive to leakage. After training behavioral and facial image data on Federated Machine Learning (FL) models, promising results are achieved, with 70% accuracy for prediction of ASD according to behavioral traits in a federated learning private environment, and a 62% accuracy is reached for prediction of ASD given an image of the patient's face.