1 October 2026 · Strasbourg, France
Co-organized the 7th MICCAI DeCaF Workshop on Distributed, Collaborative and Federated Learning
DeCaF 2026 — 7th MICCAI Workshop on Distributed, Collaborative and Federated Learning ↗
On 1 October 2026 we held the 7th MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2026) at MICCAI 2026 in Strasbourg — a series I have co-organised since its beginnings as DCL in 2020 (see also DeCaF 2024). Data sharing across hospitals is often impractical because of privacy regulations and data ownership, so DeCaF is a forum for methods that train and deploy models collaboratively without sharing raw data: federated and distributed learning, multi-agent and agentic systems across institutions, federated fine-tuning of foundation models, collaborative inference, privacy, security and fairness, and benchmarks for real-world federated learning.
Programme (local time, Strasbourg)
| Time | Session |
|---|---|
| 08:00 | Introduction to the DeCaF workshop |
| 08:10 | Poster preview (14 posters) |
| 08:25 | Keynote 1 — Evelyn Trautmann, Apheris, Germany |
| 09:15 | Keynote 2 — Marco Lorenzi, Université Côte d’Azur, France |
| 10:00 | Coffee break and poster session |
| 10:30 | Oral presentations (7 papers) |
| 12:00 | NVIDIA presentation |
| 12:15 | Awards and closing remarks |
NVIDIA sponsored a DGX Spark for the best paper and Brev credits for the runner-up. Accepted papers appear in the Springer LNCS proceedings of the MICCAI 2026 satellite events.
Organisers: Meirui Jiang (Ant Group), Xiaoxiao Li (University of British Columbia), Shadi Albarqouni (University Hospital Bonn), Spyridon Bakas (Indiana University School of Medicine and MLCommons), Holger Roth (NVIDIA), Alex Karagyris (MLCommons), Yasmeen George (Monash University) and Pramit Saha (University of Oxford). Details: workshop-decaf.github.io/decaf-2026.