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Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling

Summary

This study presents federated physics-informed neural networks (PINNs) that combine federated learning with linear elasticity equations to model brain tumor biomechanics while preserving patient privacy. Simulations across three clinical sites sharing only model weights achieved 91.4% accuracy, outperforming the pooled baseline of 90.0%, with significant improvements in pituitary adenoma accuracy.

SourcearXiv Computer VisionAuthor: Mahmuda Akter Sristy, Md Al-Mahfuz Chowdhury, Momota Ahsana Meem, Sajid Ahamed, Kazi Irfan Subhan
Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling
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[Submitted on 28 Jul 2026]

Title:Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling

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Abstract:Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft brain tissue, yet common diagnostic methods rely on static imaging that cannot capture tumor growth, tissue displacement, or changes in stiffness over time. Deep learning models for this task typically require pooling patient data at one site, which conflicts with privacy rules such as GDPR and HIPAA and limits generalization across institutions, a challenge that is pronounced in neuro oncology given patient diversity. This study presents a federated physics informed neural network combining federated learning with a physics informed loss built on the equations of linear elasticity. Three simulated clinical sites each train a local network on patient specific MRI data using a physics informed loss, and only model weights are shared with a central server through the FedAvg protocol over one hundred rounds, keeping raw data at its site of origin. The federated model reached an overall accuracy of 91.4%, against 90.0% for a non federated baseline trained on pooled data, an average AUC of 0.985 across tumor classes, and a rise in pituitary tumor accuracy from 85.6 to 94.5%. Training produced smooth, divergence free displacement fields consistent with expected tissue deformation, showing that federated training can be paired with physics based constraints without a meaningful loss in performance.

Comments: 11 pages, 9 figures

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.26207 [cs.CV]

(or arXiv:2607.26207v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.26207

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mahmuda Akter Sristy [view email] [v1] Tue, 28 Jul 2026 19:15:37 UTC (2,896 KB)

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Key points and analysis

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Key points

  • Combines federated learning with physics-informed loss
  • Trained on three simulated sites, sharing only model weights
  • Achieved 91.4% accuracy and average AUC of 0.985
  • Preserves patient privacy under GDPR and HIPAA

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