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

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.

INTERSPEECH 2023, pp. 5003--5007, Dublin, Ireland

References in corpus (2)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Cited by in corpus (5)

• Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
• Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
• The Impact of Speech Anonymization on Pathology and Its Limits
• Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
• Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data