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Privacy preserving distributed learning classifiers - Sequential learning with small sets of data.

Fadila Zerka1, Visara Urovi2, Fabio Bottari3

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PubMed
Summary
This summary is machine-generated.

Sequential learning enables AI models to train on small, private medical datasets, achieving performance comparable to centralized approaches. This distributed method enhances AI development where data is siloed, preserving patient privacy.

Keywords:
Distributed learningMedical data privacyRare diseaseSequential learning

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Area of Science:

  • Medical Artificial Intelligence
  • Distributed Machine Learning
  • Data Privacy

Background:

  • Artificial intelligence (AI) model development requires substantial high-quality data, which is often difficult to obtain from a single institution.
  • Siloed clinical and imaging datasets present challenges for training robust AI models.
  • Sequential learning offers a potential solution for leveraging small, distributed datasets.

Purpose of the Study:

  • To investigate the impact of sequential learning on AI models trained with small, siloed clinical and imaging data.
  • To evaluate if these models can achieve performance equivalent to models trained on a single, centralized database.
  • To assess the privacy-preserving capabilities of a distributed learning framework.

Main Methods:

  • A privacy-preserving distributed learning framework was proposed, utilizing sequential learning from individual datasets.
  • The framework was applied to Logistic Regression, Support Vector Machines (SVM), and Perceptron algorithms.
  • Model performance was evaluated using four open-source datasets: Breast cancer, Indian liver, NSCLC-Radiomics, and Stage III NSCLC.

Main Results:

  • The proposed distributed sequential learning framework achieved predictive performance comparable to centralized learning approaches.
  • Pairwise DeLong tests indicated no significant performance difference between the distributed and centralized models for each dataset.
  • The framework effectively trained AI models on small, siloed datasets without compromising predictive accuracy.

Conclusions:

  • Distributed sequential learning effectively preserves medical data privacy while enabling collaborative AI development.
  • This approach is valuable for scenarios where centralized data collection is logistically impossible.
  • Institutions with small, clinically valuable datasets can collaboratively train robust AI models with equivalent performance to centralized models, ensuring patient privacy.