Two-day workshop

From Zero to Hero

Federated AI in Healthcare Systems

24–25 August 2026 St John's College, Cambridge In-person event

About the workshop

Federated learning is reshaping how we build machine learning models on sensitive clinical data, allowing institutions to collaborate and learn from one another without moving patients' records.

This two-day workshop is designed to take participants from zero to hero, whatever their starting point. The programme combines keynotes, research talks, hands-on tutorials, panel discussions, and live demonstrations exploring the future of federated AI in healthcare.

Whether you are a clinician curious about the methods, a researcher deploying federated systems, or someone who has only just heard the term, the workshop offers an opportunity to learn, ask questions, and connect with others working in the field.

What to expect

Participants will hear from experts working at the frontier of federated healthcare AI, work through prepared tutorial notebooks, and explore practical issues involved in moving from research pilots to real clinical systems.

Everyone who participates will receive a certificate of attendance.

From Zero to Hero Tutorials

Session 1 Tutorial Notebook

Interactive Colab notebook – run the code right in your browser

Open In Colab

Session 1 covers training and evaluating a local machine learning model for a clinical detection task on real healthcare data.

Session 2 Federated AI App

Flower Hub – discover, run, and verify federated AI apps across heterogeneous environments

Open Flower Hub App

Session 2 explores federated learning across distributed healthcare data using a live Flower Hub app. The app demonstrates decentralized verification and execution of federated AI models for iron deficiency detection without centralizing patient records.

Organising committee

  • Fan Zhang — Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
  • Daniel Kreuter — Precision Healthcare University Research Institute, Queen Mary University of London, UK
  • Shiqiang Wang — CORE-AIx, Department of Computer Science, University of Exeter, UK
  • Gregory Verghese — PharosAI, UK
  • Nicholas Lane — Flower Labs and Department of Computer Science and Technology, University of Cambridge, UK
  • Kapal Dev — Department of Computer Science, Munster Technological University, Ireland
  • Javier Fernandez-Marques — Flower Labs, UK
  • Carola-Bibiane Schönlieb — Cambridge Image Analysis, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
  • Michael Roberts — BloodCounts! and Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK

Workshop programme

Use the PDF controls to zoom, search, print, and navigate pages.

Open programme PDF