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

Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages