  Parkinson's disease (PD) is a neurological disorder impacting a person's
speech. Among automatic PD assessment methods, deep learning models have gained
particular interest. Recently, the community has explored cross-pathology and
cross-language models which can improve diagnostic accuracy even further.
However, strict patient data privacy regulations largely prevent institutions
from sharing patient speech data with each other. In this paper, we employ
federated learning (FL) for PD detection using speech signals from 3 real-world
language corpora of German, Spanish, and Czech, each from a separate
institution. Our results indicate that the FL model outperforms all the local
models in terms of diagnostic accuracy, while not performing very differently
from the model based on centrally combined training sets, with the advantage of
not requiring any data sharing among collaborators. This will simplify
inter-institutional collaborations, resulting in enhancement of patient
outcomes.
