5th MICCAI Workshop on
“Distributed, Collaborative and Federated Learning”
Important Dates
- Location: MARRAKESH / MOROCCO
- Submission Deadlines: 30th of June 2024 AOE
- Notification of Acceptance: 15th of July 2024
- Camera Ready: 23th of July 2024
- Workshop Date: 10th of October 2024 (from 1:30 PM to 6:00 PM)
- Workshop room: Conference Center-Borj 6
News
[October] Let's meet at Borj 6 @Conference Center!
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 mode generalizability? 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 the 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 2024 template through CMT (https://cmt3.research.microsoft.com/DeCaF2024/). Topics include but are not limited to:
- Federated, distributed learning, and other forms of collaborative learning
- Server-client and peer-to-peer learning
- Advanced data and model parallelism learning techniques
- Optimization methods for distributed or collaborative learning
- Privacy-preserving technique and security for distributed, federated, and collaborative learning
- Efficient communication and learning (multi-device, multi-node)
- Adversarial, inversion and other forms of attacks on distributed , federated, and collaborative learning
- Dealing with unbalanced (non-IID) data in federated and collaborative learning
- Diverse decentralized medical imaging data analysis
- Security-auditing system for federated learning
- Asynchronous learning
- Software tools and implementations of distributed, federated, and collaborative learning
- Model sharing techniques, sparse/partial learning of models
- Applications of federated/distributed/collaborative learning techniques: multi-task learning, model agnostic learning, meta-learning, etc.
Program
Keynote Session
Daniel Truhn
University Hospital of Aachen, Germany
Daniel Truhn is a professor of medicine and a physicist. He works as a radiologist at the University Hospital of Aachen and leads the group for AI in Medicine which develops AI models for use in clinical routine.
Topic: Overcoming Barriers to Implementing Federated Machine Learning in Healthcare: Policy Options for Ethical and Secure Data Collaboration
Laurent Condat
King Abdullah University of Science and Technology, Saudi Arabia
Laurent Condat received a PhD in applied mathematics in 2006 from Grenoble Institute of Technology, Grenoble, France. After 2 years as a postdoc in the Helmholtz Zentrum Muenchen, Munich, Germany, he was hired as a permanent researcher by the French National Center for Scientific Research (CNRS). He worked in the GREYC, Caen and from 2012 in GIPSA-Lab, Grenoble. From 2016 to 2019, he was a member of the French National Committee for Scientific Research. Since Nov. 2019, he is on leave from the CNRS and a Senior Research Scientist in King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Dr. Condat's main area of interest is optimization and its application to signal and image processing, and machine learning. He is a senior member of the IEEE and an associate editor of IEEE Transactions on Signal Processing. He received a best student paper award at IEEE ICIP 2005, a best PhD award from Grenoble Institute of Technology, and several meritorious reviewer awards. Since 2020, he has been in Stanford's list of the world's top 2% most influential scientists.
Topic: Communication-efficient Distributed Optimization Algorithms
Meet the Organising Team
Contact: xiaoxiao.li(at)ece.ubc.ca
Shadi Albarqouni
University Hospital Bonn | Helmholtz Munich, Germany
Xiaoxiao Li
University of British Columbia, Canada
Nicola Rieke
NVIDIA, Germany
Spyridon Bakas
Indiana University, USA
Qi Dou
Chinese University of Hong Kong, China
Holger Roth
NVIDIA, USA
Program Committee Members
Anabik Pal, IISER Berhampur
Anna Banaszak, Technical University of Munich
Chamani Shiranthika Jayakody Kankanamalage, Simon Fraser University
Di Fan, USC
Guangyao Zheng, Rice University
Herve Delingette, Inria
Jonny Hancox, NVIDIA
Kevinminh Ta, Yale University
Lucia Innocenti, INRIA, King's College London
Malte Tölle, University Hospital Heidelberg
Marawan Elbatel, The Hong Kong University of Science and Technology
Muzaffer Özbey, Bilkent University
Nikhil J Dhinagar, Imaging Genetics Center, University of Southern California
Onat Dalmaz, Stanford University
Pramit Saha, University of Oxford
Ralf Floca, German Cancer Research Center (DKFZ)
Ruoyou Wu, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Shanshan Wang, Shenzhen Institute of Advanced Technology, Chinese Academy of Science
Shunxing Bao, Vanderbilt University
Sourav Kumar, Massachusetts General Hospital
Tolga Cukur, Bilkent University
Xiaoran Zhang, Yale University
Zeju Li, Imperial College London
Zhao Wang, The Chinese University of Hong Kong
Meet the Outreach Comittee Team
Contact: chunyinh(at)ece.ubc.ca, david.duenas-gaviria@ukbonn.de, tiantianzhang@cuhk.edu.hk
Chun-Yin Huang
University of British Columbia, Canada
David D. Gaviria
University Hospital Bonn, Germany
Tiantian Zhang
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 2024 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 2024 will be published as part of the joint MICCAI Workshops proceedings with Springer (LNCS)
