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

• URL: http://arxiv.org/abs/2305.11284v2
• Date: Mon, 21 Aug 2023 09:35:20 GMT
• Title: Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
• Authors: Soroosh Tayebi Arasteh, Cristian David Rios-Urrego, Elmar Noeth, Andreas Maier, Seung Hee Yang, Jan Rusz, Juan Rafael Orozco-Arroyave
• Abstract summary: 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. 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.
• Score: 10.04992537510352
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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