# A federated learning consortium of cancer centres that jointly own the models

Source: https://onco.cc/ideas/idea-fund-federated-learning-consortium/  
OnCo record `idea-fund-federated-learning-consortium` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Hospitals could train shared AI models on all their patients' scans and records without any data leaving the building, and jointly own the results, if someone built and governed the network.

## Summary

A consortium of cancer centres and trial groups operating a federated learning infrastructure (models travel, data stay) for pathology, radiology and multimodal outcome prediction, with a governance agreement that gives members joint ownership of trained models, shared validation protocols and a route to regulatory submission. Owkin's federated networks and the EU's several federated health data projects show technical feasibility; the missing piece is a durable, member-owned institution with IP terms that reward data contribution rather than data extraction. Prospective validation of consortium models in member trials closes the loop with the AI validation bottleneck.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- Hypothesis: A member-owned federated consortium of twenty centres trains models that outperform single-centre models on held-out external validation for at least three clinical tasks within three years, and at least one consortium model enters a prospective clinical trial.
- Rationale: Federated training has matched centralised training in published pathology and radiology tasks; the barrier to scale is governance and IP, not algorithms. Member ownership addresses the concern that centres give away data value to vendors.
- Proposed test: Constitute the consortium with a governance charter, run three federated training tasks against single-centre baselines and pre-register external validation.
- Maturity: early-clinical
- Actor: engineering

## Sources

- Bottleneck evidence (Secrecy and intellectual property block collaboration): Danchev et al., Evaluation of data sharing after implementation of the ICMJE data sharing statement requirement (JAMA Netw Open 2021): https://doi.org/10.1001/jamanetworkopen.2020.33972

## Connected records

- ideas: [A single oncology trial data trust with mandatory deposit within eighteen months](https://onco.cc/ideas/idea-fund-trial-data-trust/), [Patient-level multimodal foundation models for treatment selection](https://onco.cc/ideas/idea-multimodal-foundation-model/)
- technologies: [AI in radiology](https://onco.cc/technologies/radiology-ai-screening/), [Digital pathology & AI](https://onco.cc/technologies/digital-pathology-ai/), [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/)
- companies: [Owkin](https://onco.cc/companies/owkin/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Data silos](https://onco.cc/bottlenecks/b-data-silos/), [Secrecy and intellectual property block collaboration](https://onco.cc/bottlenecks/b-ip-collaboration/)
- key papers: [Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement](https://onco.cc/key-papers/paper-danchev-jama-netw-open/)

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