  Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting
from alterations in the embryological brain before birth. This disorder
distinguishes its patients by special socially restricted and repetitive
behavior in addition to specific behavioral traits. Hence, this would possibly
deteriorate their social behavior among other individuals, as well as their
overall interaction within their community. Moreover, medical research has
proved that ASD also affects the facial characteristics 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 federated learning scheme to predict ASD in a certain
individual based on their behavioral and facial features, embedding a merging
process of both data features through facial feature extraction while
respecting patient data privacy. After training behavioral and facial image
data on federated machine learning models, promising results are achieved, with
70\% accuracy for the prediction of ASD according to behavioral traits in a
federated learning environment, and a 62\% accuracy is reached for the
prediction of ASD given an image of the patient's face. Then, we test the
behavior of regular as well as federated ML on our merged data, behavioral and
facial, where a 65\% accuracy is achieved with the regular logistic regression
model and 63\% accuracy with the federated learning model.
