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Improving personalized federated learning to optimize site-specific performance in computer-aided

Aiki Yamada1,2, Shouhei Hanaoka2, Tomomi Takenaga2

  • 1Chiba University, Graduate School of Science and Engineering, Department of Medical Engineering, Chiba, Japan.

Journal of Medical Imaging (Bellingham, Wash.)
|October 24, 2025
PubMed
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Improved Ditto enhances personalized federated learning (PFL) for medical imaging AI. This method stabilizes model updates and reduces tuning costs for computer-aided detection/diagnosis (CAD) software.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Medical Imaging

Background:

  • Personalized federated learning (PFL) addresses data heterogeneity and privacy in AI.
  • Ditto, a PFL method, faces challenges with unstable updates and high tuning costs.
  • PFL applications in computer-aided detection/diagnosis (CAD) software are under investigation.

Purpose of the Study:

  • To introduce Improved Ditto, a novel PFL method.
  • To enhance stability and reduce hyperparameter tuning costs in PFL.
  • To apply PFL to computer-aided detection/diagnosis (CAD) software.

Main Methods:

  • Developed a personalized model update rule for Improved Ditto.
  • Dynamically adjusted global model weights based on L2-norm of gradient and global model terms.
Keywords:
brain metastasiscerebral aneurysmcomputer-aided detection/diagnosisfederated learningliver lesionpersonalized federated learning

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  • Evaluated Improved Ditto on three CAD tasks: aneurysm detection, metastasis detection, and liver lesion classification.

    • Main Results:

    • Improved Ditto demonstrated competitive performance against Ditto and other federated learning methods in two of three CAD tasks.
    • Achieved a reduced hyperparameter search space, lowering tuning costs.
    • Enhanced the stability of personalized model updates, indicating improved adaptability.

    Conclusions:

    • Dynamically adjusting global model weights improves PFL stability and adaptability.
    • Improved Ditto offers reduced hyperparameter tuning costs for PFL.
    • The proposed method shows potential benefits for computer-aided detection/diagnosis (CAD) software.