FLH — Summer 2021

Mon, 15 Mar 2021 00:00:00 +0000

Organizers: , and , , Chair for , and , Chair for ,

Time: Fridays, 10:00 - 12:00

Announcements

14-07-2021: The deadline of the blog post is extended to Thursday, 22.07.2021.
14-04-2021: Papers are assigned. Please have a look at the table below.
08-04-2021: Please send 3-4 preferences from different topics (Data Heterogeneity, Robustness, …etc.) to by 13th April 2021.
29-01-2021: Preliminary meeting is moved to Friday, 05.02.2021 (11:00-11:30). Please register in advance for this meeting through this . After the registration, you will receive a confirmation email with the dial-up information.
19-01-2021: Preliminary meeting: ~~Monday, 01.02.2021 (11:00-11:30)~~ via Zoom
19-01-2021: Contact information: if you have any questions about this seminar, please feel free to contact
19-01-2021: The website is up!

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 in the 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 themselves if the maximum number of participants hasn’t been reached (please pay attention to the ).
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 the presentation two weeks right after your presentation session. Please push your slides to the respective folder, i.e., SS2021, at this , 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 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 for your presentation and blog posts.

Schedule

Date (tentative)	Session: Topic	Speakers / Presenters
05.02.2021 (11:00 AM)	Preliminary Meeting	Shadi
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
28.05.2021	–	–
11.06.2021	Robustness	Stampfl, Mostafa
25.06.2021	Federated Learning in Healthcare: from Theory to Practice	Dr. , Inria, France
09.07.2021	Uncertainty and Interpretability	Yu, Feil

List of Topics and Papers

Topic	No	Title	Conference/Journal	Student (Last name)
Data Heterogeneity	1	FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space	CVPR 2021
	2	Personalized Federated Learning using Hypernetworks	arxiv 2021	Buess
	3	Adaptive Federated Optimization	ICLR 2021
	4	FedMix: Approximation of Mixup Under Mean Augmented Federated Learning	ICLR 2021
	5	HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients	ICLR 2021	Yirik
	6	Personalized Federated Learning with First Order Model Optimization	ICLR 2021	Buchgberger
	7	Federated Learning Based on Dynamic Regulariation	ICLR 2021
	8	Tackling the Objective Inconsistency Problem in Heterogenous Federated Optimization	NeurIPS 2020
	9	Ensemble Distillation for Robust Model Fusion in Federated Learning	NeurIPS 2020
	10	Salvaging Federated Learning by Local Adaptation	arxiv 2020	Flecken
	11	FedAwS: Federated Learning with Only Positive Labels	ICML 2020

Robustness	12	Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective	CVPR 2021
	13	Untargeted Poisoning Attack Detection in Federated Learning via Behaviour Attestation	arxiv 2021
	14	Privacy and Robustness in Federated Learning: Attacks and Defenses	arxiv 2020	Stampfl
	15	How to backdoor federated learning	PMLR 2020	Mostafa
	16	DBA: Distributed Backdoor Attacks against Federated Learning	ICLR 2020

Privacy and Security	17	Federated f-Differential Privacy	PMLR 2021
	18	BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning	USENIX 2020
	19	Secure Weighted Aggregation in Federated Learning	arxiv 2020
	20	Differentially Private Meta-Learning	ICLR 2020
	21	Secure, privacy-preserving and federated machine learning in medical imaging	Nature 2020

Uncertainty and Interpretability	22	Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data	Journal of Healthcare Informatics Research 2021
	23	FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning	ICLR 2021	Yu
	24	Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms	ICLR 2021	Feil
	25	Probabilistic predictions with federated learning	2020
	26	Interpret Federated Learning with Shapley Values	arxiv 2019

Interested?

Join our Federated Learning Working Group at

Contact the organizers

If you have any questions regarding the course, please do not hesitate to contact

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