mlsanityData to 7 Oct 2026

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

arXiv 2305.11284 ↗First posted 18 May 2023eess.AS cs.AI cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

14 of 34 references.

References · 14

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2 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.

9 papers.

  1. 2024The Impact of Speech Anonymization on Pathology and Its Limitscited by 11
  2. 2024Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Reviewcited by 7
  3. 2025Differential privacy for medical deep learning: methods, tradeoffs, and deployment implicationscited by 7
  4. 2023Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacycited by 6
  5. 2025Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representationscited by 6
  6. 2024Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient datacited by 4
  7. 2025Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognitioncited by 1
  8. 2025Perceptual implications of automatic anonymization in pathological speechcited by 0
  9. 2026Towards Personalized Federated Learning for Dysarthric Speech Recognitioncited by 0