#PrivacyPreservingAI

Jorge Miguel Silvajorgemfs
2025-07-29

πŸ“’ Just published our new work on federated random forests for privacy-preserving machine learning!
πŸ“„ β€œA Federated Random Forest Solution for Secure Distributed Machine Learning”
πŸ“Œ IEEE: doi.org/10.1109/CBMS65348.2025

πŸ“‚ Supplementary slides:
πŸ”— doi.org/10.5281/zenodo.16539345

We're advancing secure AI without sharing data. Feedback & collaborations welcome! πŸš€

CSBJcsbj
2025-06-26

πŸ” Can data privacy and AI innovation truly coexist in healthcare?

πŸ”— Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions. Computational and Structural Biotechnology Journal, DOI: doi.org/10.1016/j.csbj.2025.06

πŸ“š CSBJ Smart Hospital: csbj.org/smarthospital

Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.06.009
CSBJcsbj
2025-03-27

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

πŸ”— Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data. Computational and Structural Biotechnology Journal, DOI: doi.org/10.1016/j.csbj.2025.03

πŸ“š CSBJ Smart Hospital: csbj.org/smarthospital

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

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