One of the biggest challenges in #AI and #healthcare is how to collaboratively train large AI models from multiple hospitals without compromising patient privacy? Thrilled to introduce our new paper, DeCaPH, which is now officially published in @TheLancet @eBioMedicine! **Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data** What does DeCaPH do? 🔒 Privacy-preserving collaborative ML: Train together without sharing sensitive data. 🌐 Decentralization: No central server is needed, reducing privacy risks. 🚫 Robust against privacy attacks: Designed for healthcare's unique privacy needs. **Highlights** --- Tailored for healthcare setting: Designed specifically for hospitals, DeCaPH caters to their unique threat models and privacy requirements, achieving the optimal utility-privacy trade-off. --- Potential Real-world Impact: We conducted comprehensive evaluations on three cross-silo datasets for three distinct tasks: clinical outcome prediction from electronic health records, cell classification with single-cell RNA transcriptomics, and pathology identification using chest radiology images. --- Robust to privacy attacks: Our evaluations show that models trained with DeCaPH are more resilient against privacy attacks than those without privacy protection. Kudos to Emmy Fang (@EmmyFangCY), who is an amazing PhD student cosupervised by me and @NicolasPapernot , for her incredible leadership in this project! Read More: Paper: sciencedirect.com/science/ar… arXiv: arxiv.org/abs/2402.00205 Code: github.com/cleverhans-lab/De… @VectorInst @UofTCompSci @UofT_LMP @UHN @bradwouters @drbarryrubin