Is Federated Learning the Answer to Healthcare's Privacy Dilemma?

Machine learning in healthcare needs patient data for better outcomes while maintaining privacy. Federated learning trains models across decentralized devices without sharing raw data!

The elegance? Patient data never leaves the hospital, yet everyone benefits.

Let's discuss implementing this approach for your sensitive data challenges—reach out for a consultation today!

This study explores how Federated Learning (FL) can revolutionize medical AI by enhancing generalizability while preserving patient privacy.

Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.03.031

CSBJ Smart Hospital: https://www.csbj.org/smarthospital

This study introduces APPFLx, a secure and scalable Federated Learning (FL) framework designed to accelerate privacy-preserving biomedical research across institutions with heterogeneous computing environments.

Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx. DOI: https://doi.org/10.1016/j.csbj.2024.12.001

CSBJ Smart Hospital: https://www.csbj.org/smarthospital

Time for a proper #introduction! I'm a #MachineLearning researcher at Inria, France, focusing on #TrustworthyAI—especially #decentralization & #privacy. I design algorithms that learn from #decentralized data (#FederatedLearning) without memorizing personal data (#DifferentialPrivacy), working toward putting #data & #AI back in people's hands. Maybe one day, these ideas could be used in the #Fediverse!

Project announcement : "PROSurvival" builds a collaborative federated learning framework to predict survival in prostate cancer patients.

Partners are OFFIS - Institute for Information Technology, Charité, Goethe University Frankfurt, and Fraunhofer MEVIS.

Here's the gist: In the long run, we want to find predictive image features that can be identified in tissue sections from routine diagnostics.

#federatedlearning #ai #foundationmodels #ComputationalPathology

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