6th MICCAI Workshop on
“Distributed, Collaborative and Federated
Learning”
Important Dates
- Location: DAEJEON / REPUBLIC OF KOREA
- Submission Deadlines: 27th of June 2025 AOE
News
[March] The DeCaF Workshop has been approved for MICCAI 2025
Distributed, Collaborative and Federated Learning
Deep learning, AI's fastest-growing field, empowers enormous advances in
applications in both science and real-world scenarios. It has reached a
consensus that models could be further improved with a growing amount of
data. However, enabling learning on these huge datasets or training huge
models in a timely manner requires distributing the learning on several
devices. One particularity in the medical imaging setting is that data
sharing across different institutions often becomes impractical due to
strict privacy regulations as well as data ownership concerns, making
the collection of large-scale diverse centralized datasets practically
impossible.
Some of the problems, therefore, become: how can we train models in a
federated way on several devices? And is it possible to achieve models
as strong as those that can be trained on large centralized datasets
without sharing data and breaching the restrictions on privacy and
property? How can we ensure data privacy and model fairness? How can we
handle multi-site heterogeneous data? Federated learning (FL) allows
different institutions to contribute to building more powerful models by
performing collaborative training without sharing any training data. The
trained model can be distributed across various institutions instead of
the actual data. We hope that with FL and other forms of distributed and
collaborative learning, the objective of training better and more robust
models with higher clinical utility while protecting privacy within the
data can be achieved.
Call for Papers
Through the fourth MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCAF), we aim to provide a discussion forum to compare, evaluate and discuss methodological advancements and ideas around federated, distributed, and collaborative learning schemes that are applicable in the medical domain. We invite full paper (8-page) submissions using the MICCAI 2025 template through CMT (https://cmt3.research.microsoft.com/DeCaF2025/). Topics include but are not limited to:
- Federated, distributed learning, and other forms of collaborative learning
- Federated learning techniques for training large-scale foundation models
- Impact of data and compute resource heterogeneity in federated learning
- Optimization methods for distributed or collaborative learning
- Model sharing techniques, sparse/partial learning of models
- Fairness and bias mitigation technique for federated or collaborative learning
- Privacy-preserving technique and security for distributed, federated, and collaborative learning
- Advanced data and model parallelism learning techniques
- Resource-efficient federated learning with foundation models
- Attack and defense on federated or collaborative learning
- Medical imagine data and benchmark for federated or collaborative learning
- Applications of federated/distributed/collaborative learning techniques: multi-task learning, model agnostic learning, meta-learning, etc.
Program
To be announced...
Keynote Session
To be announced...
Meet the Organising Team
Contact: mrjiang(at)cse.cuhk.edu.hk
Shadi Albarqouni
University Hospital Bonn | Helmholtz Munich, Germany
Xiaoxiao Li
University of British Columbia, Canada
Nicola Rieke
NVIDIA, Germany
Spyridon Bakas
Indiana University, USA
Meirui Jiang
Chinese University of Hong Kong, China
Holger Roth
NVIDIA, USA
Program Committee Members
To be announced...
Meet the Outreach Comittee Team
Contact: Elodie.Germani(at)ukbonn.de, yuanzhong(at)link.cuhk.edu.hk
Elodie Germani
University Hospital Bonn, Germany
Yuan Zhong
Chinese University of Hong Kong, China
Sponsor
NVIDIA offers GPU resources to all accepted papers.
Submission Guidelines
Format: Papers will be submitted electronically following Lecture Notes in Computer Science (LNCS) style of up to 8 + 2 pages (same as MICCAI 2025 format). Submissions exceeding page limit will be rejected without review. Latex style files can be found from Springer, which also contains Word instructions. The file format for submissions is Adobe Portable Document Format (PDF). Other formats will not be accepted.
Double Blind Review: DeCaF reviewing is double blind. Please review the Anonymity guidelines of MICCAI main conference, and confirm that the author field does not break anonymity.
Paper Submission: DeCaF uses the CMT system for online submission.
Supplemental Material: Supplemental material submission is optional, following same deadline as the main paper. Contents of the supplemental material would be referred to appropriately in the paper, while reviewers are not obliged to read them.
Submission Originality: Submissions should be original, no paper of substantially similar content should be under peer review or has been accepted for a publication elsewhere (conference/journal, not including archived work).
Proceedings: The proceedings of DeCaF 2025 will be published as part of the joint MICCAI Workshops proceedings with Springer (LNCS)
