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Federated learning trains one AI model across hospitals by exchanging model updates, not patient data, so a pathology or radiology model learns from every site while records stay behind each firewall. Owkin, NVIDIA FLARE and the MELLODDY pharma consortium use it; governance overhead and differing data across sites are the practical obstacles.
Federated learning (NVIDIA FLARE, Owkin's Substra, Rhino Health, Intel OpenFL) trains a shared model on data held locally at each institution; used for pathology and radiology models (Owkin-led projects, the EXAM COVID model, Flywheel), and for pharma consortia (MELLODDY). Complementary tools include differential privacy, synthetic data (MDClone, Syntegra), and trusted execution environments. Governance and validation on heterogeneous data are the practical challenges.
Model updates, not data, are exchanged and aggregated centrally; privacy techniques limit what updates can reveal.
Query for this technology: (TITLE:"Federated learning and privacy-preserving AI" OR ABSTRACT:"Federated learning and privacy-preserving AI") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Federated learning and privacy-preserving AI, not a curated reading list.
Shares AI compute and model platforms for oncology, Pathology & radiology foundation models and the tag supporting.
Shares AI compute and model platforms for oncology, AI in radiology and the tag supporting.
Shares Oncology EHR and real-world data platforms and the tag supporting.
Shares Oncology EHR and real-world data platforms and the tag supporting.
Shares Oncology EHR and real-world data platforms and the tag supporting.
Shares Oncology EHR and real-world data platforms and the tag supporting.
Shares Oncology EHR and real-world data platforms and the tag supporting.
Shares Oncology EHR and real-world data platforms and the tag supporting.
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The federated learning platform that trained a glioblastoma segmentation model across 71 sites without sharing patient data.