Federated Learning in Healthcare (SoSe2021)

Master Seminar (IN2107, IN4410) (2 SWS, 5 ECTS) offered for BioMedical Computing (BMC) program at the Chair for Computer Aided Medical Procedures and Augmented Reality, TU Munich

Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, Prof. Nassir Navab, Chair for Computer Aided Medical Procedures, and Prof. Daniel Rueckert, Chair for AI in Medicine, TU Munich

Tutors: Cosmin Bercea

Time: Fridays, 10:00 - 12:00




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.



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:

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 the presentation two weeks right after your presentation session. Please push your slides to the respective folder, i.e., SS2021, at this repository, and create your blog at the Wiki section under Blogs. The deadline of the blog post is extended to Thursday, 22.07.2021.

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.


Date (tentative) Session: Topic Speakers / Presenters Slides
05.02.2021 (11:00 AM) Preliminary Meeting Shadi Slides
Online Paper Assignment Shadi/Cosmin
16.04.2021 Guidelines Shadi
30.04.2021 Data Heterogeneity I Buess, Yirik
14.05.2021 Data Heterogeneity II Buchberger, Flecken
11.06.2021 Robustness Stampfl, Mostafa
25.06.2021 Federated Learning in Healthcare: from Theory to Practice Dr. Marco Lorenzi, Inria, France
09.07.2021 Uncertainty and Interpretability Yu, Feil

List of Topics and Papers

Topic No Title Conference/Journal Tutor Student (Last name) Link
Data Heterogeneity 1 FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space CVPR 2021 PDF
2 Personalized Federated Learning using Hypernetworks arxiv 2021 Buess PDF
3 Adaptive Federated Optimization ICLR 2021 PDF
4 FedMix: Approximation of Mixup Under Mean Augmented Federated Learning ICLR 2021 PDF
5 HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients ICLR 2021 Yirik PDF
6 Personalized Federated Learning with First Order Model Optimization ICLR 2021 Buchgberger PDF
7 Federated Learning Based on Dynamic Regulariation ICLR 2021 PDF
8 Tackling the Objective Inconsistency Problem in Heterogenous Federated Optimization NeurIPS 2020 PDF
9 Ensemble Distillation for Robust Model Fusion in Federated Learning NeurIPS 2020 PDF
10 Salvaging Federated Learning by Local Adaptation arxiv 2020 Flecken PDF
11 FedAwS: Federated Learning with Only Positive Labels ICML 2020 PDF
Robustness 12 Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective CVPR 2021 PDF
13 Untargeted Poisoning Attack Detection in Federated Learning via Behaviour Attestation arxiv 2021 PDF
14 Privacy and Robustness in Federated Learning: Attacks and Defenses arxiv 2020 Stampfl PDF
15 How to backdoor federated learning PMLR 2020 Mostafa PDF
16 DBA: Distributed Backdoor Attacks against Federated Learning ICLR 2020 PDF
Privacy and Security 17 Federated f-Differential Privacy PMLR 2021 PDF
18 BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning USENIX 2020 PDF
19 Secure Weighted Aggregation in Federated Learning arxiv 2020 PDF
20 Differentially Private Meta-Learning ICLR 2020 PDF
21 Secure, privacy-preserving and federated machine learning in medical imaging Nature 2020 PDF
Uncertainty and Interpretability 22 Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data Journal of Healthcare Informatics Research 2021 PDF
23 FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning ICLR 2021 Yu PDF
24 Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms ICLR 2021 Feil PDF
25 Probabilistic predictions with federated learning 2020 PDF
26 Interpret Federated Learning with Shapley Values arxiv 2019 PDF


Contact the organizers

If you have any questions regarding the course, please do not hesitate to contact Dr. Shadi Albarqouni