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CBMS Special Track 2022 : Federated Learning for Medical Data


When Jul 21, 2022 - Jul 23, 2022
Where Shenzhen, China (or virtual attendance)
Submission Deadline Apr 25, 2022
Notification Due May 23, 2022
Final Version Due Jun 6, 2022
Categories    federated learning   medical data

Call For Papers

CBMS Special Track - Federated Learning for Medical Data
21-23 July 2022
Shenzhen, China (virtual attendance will be possible)


Important Dates
* Paper submission deadline: 25 April 2022
* Notification of acceptance: 23 May 2022
* Camera-ready due: 6 June 2022
All submissions close at 11:59 pm Anywhere On Earth [AOE]

About the Special Track - Federated Learning for Medical Data
This Special Track is organized as part of the 35th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

The success of modern machine learning is based on access to rich, diverse, and, above all, large data sets. However, getting access to large datasets can be a challenge in many domains, one of which is the medical domain. Different institutions, such as hospitals, medical centers, and pharmaceutical companies, often own the data, and there are straight privacy and regulatory constraints when sharing such data. Moreover, medical data is sometimes collected by IoT devices with their limited inherent communication and privacy constraints. Hence, despite the benefits of machine learning in such distributed data settings, it is essential that the training be done locally without sharing any data, but by resorting to distributed optimization solutions, such as Federated Learning. Federated learning for medical data is now in its infancy, while medical data has many unique challenges, e.g., in terms of data-owners, regulatory concerns, data diversity, algorithmic fairness, and biases. The goal of this special track is to focus on recent advancements around federated learning in such medical settings.

Topics of interest
The topics of this special track include but are not be limited to the following:

- Federated transfer learning for medical data sources
- Privacy-preserving techniques for federated learning
- Architectures and protocols for federated learning on medical data sources
- Explainable federated learning medical data sources
- Federated learning for univariate and multivariate medical time series
- Federated learning for time series nowcasting and forecasting
- Handling data diversity in federated learning architectures
- Federated learning for medical IoT
- Personalization in federated learning
- Heterogeneous and unbalanced (non-IID) medical data
- Federated MRI and medical image processing

How to Submit
Authors can submit both long/regular and short papers. Long papers may consist of up to six (6) Letter-sized pages. Long papers will be presented orally. The program committee may suggest the presentation of the paper as a short paper. Short papers may consist of up to four (4) Letter-sized pages. Short papers will be presented orally as short talks.

Each contribution must be prepared following the IEEE two-column format; the authors may use LaTeX or Microsoft Word templates when preparing their manuscripts.

Papers must be submitted electronically using the EasyChair conference management system at:

All submissions will be peer-reviewed by up to three reviewers of the Program Committee.

All accepted papers will be included in the conference proceedings and will be published by IEEE. Publication in proceedings is conditioned to the registration and presentation of the paper at the conference by one of their authors. If the paper is not presented at the conference, it will not be included in the proceedings.

Sindri Magnússon, Stockholm University
Ioanna Miliou, Stockholm University
Panagiotis Papapetrou, Stockholm University

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