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Related Experiment Videos

Client Participation per Round in Federated Learning for Multiple Sclerosis with Real-World Data.

Ashkan Pirmani1,2,3, , Yves Moreau1

  • 1ESAT - STADIUS, KU Leuven, Leuven, Belgium.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...

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Federated learning for multiple sclerosis disability prediction shows that reducing client participation (k) shortens runtime. A participation rate of approximately 0.6 maintained high performance while significantly reducing computational time.

Area of Science:

  • Artificial Intelligence
  • Neuroscience
  • Medical Informatics

Background:

  • Federated learning (FL) enables collaborative machine learning without centralizing sensitive data.
  • Predicting multiple sclerosis (MS) disability progression is crucial for patient management.
  • Routine clinical data offers a valuable resource for developing predictive models.

Purpose of the Study:

  • To investigate the impact of client participation rate (k) on federated learning performance and runtime.
  • To evaluate the trade-offs between participation levels and model accuracy for predicting MS disability progression.

Main Methods:

  • Utilized original study data, preprocessing, model architecture, and evaluation protocols.
  • Varied the per-round client participation rate (k) to values of 1.0, 0.6, and 0.4.
Keywords:
Client ParticipationFederated LearningHealth InformaticsMultiple SclerosisReal-world data

Related Experiment Videos

  • Assessed model performance using ROC-AUC and AUC-PR metrics and measured runtime.

    • Main Results:

    • Lowering client participation (k) reduced computational wall time.
    • A participation rate of k ≈ 0.6 maintained nearly all predictive performance (ROC-AUC and AUC-PR).
    • Runtime was reduced by approximately one-third with k ≈ 0.6 compared to full participation (k=1.0).

    Conclusions:

    • Client participation rate is a critical parameter influencing federated learning efficiency.
    • Optimizing k, such as to ≈ 0.6, offers a practical balance between runtime reduction and predictive accuracy in MS disability progression models.
    • Federated learning with optimized participation is a viable approach for leveraging routine clinical data in neurological disease research.