Federated Learning in Healthcare (WiSe2020)

Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, and Prof. Nassir Navab, TU Munich.

Announcements

Introduction

Following the great success of our on-going seminar on Deep Learning for Medical Applications, we would like to discuss advanced topics that are quite relevant to Federated Learning which becomes an interesting and hot research direction in the community. In simple words, Federated Learning enables training models at the client-side while preserving their privacy, and aggregates the knowledge from the nodes to learn a global model. The interesting part here that the data are kept private and not transmitted to any other nodes. Instead, the characteristics (e.g. parameters) of the global model are shared with the clients, and once the training is done locally, the characteristics are sent back to the global one for aggregation. This learning paradigm has been received quite nicely in the community, in particular, for sensitive domains, e.g. Healthcare. To push this momentum, we proposed, together with our academia and industry partners, a workshop on Federated, Collaborative, and Distributed Learning in the International Conference on Medical Image Computing and Computer-Aided Intervention (MICCAI) to attract significant contributions attacking the challenges in Medical Imaging and Healthcare. In this seminar, we will be discussing the relevant papers on Federated Learning with an emphasis on the papers tackling the common challenges in Medical Imaging, e.g. data heterogeneity, domain shift, and non-iid distributed data.

Registration

• Interested students should attend the preliminary meeting to enlist in the course.
• Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn’t been reached (please pay attention to the Deadlines).
• A maximum number of participants: 12.

Requirements

In this Master Seminar, each student is asked to send three preferences from the list, then he will be assigned one paper. In order to successfully complete the seminar, participants have to fulfill these requirements:

• Presentation: The selected paper is presented to the other participants.
• Blog Post: A blog post of 1000-1500 words excluding references should be submitted before the deadline.
• Attendance: Participants have to participate actively in all seminar sessions.

The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The blog post must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both the blog post and presentation have to be written in English.

Submission Deadline: You have to submit both the presentation two weeks right after your presentation session. The deadline of the blog post is moved to 1st Feb. 2021.

Guidelines: I could not find better than this guidelines to prepare for your presentation. The only difference is that you need to plan for 30 minutes for 1-4, and 10 minutes for 5). Nevertheless, I have prepared a few slides acting as a guidelines for your presentation and blog posts.

Schedule

List of Topics and Papers

Interested?

• Join our Federated Learning Working Group at Medical Open Network for AI (MONAI)

Contact us

If you have any questions regarding the course, please do not hesitate to contact us at shadi.albarqouni@tum.de