TeachingWs20FLH

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Hauptseminar/Master Seminar: Federated Learning in Healthcare

Prof. Nassir Navab, Dr. Shadi Albarqouni, Agnieszka Tomczak, Hasan Sarhan

Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 5 Credits
Location: Mondays
Time: 10-12
Course Language: English

Announcements

  • 30-09-2020: Your name has been assigned to one of our sessions. Please pin the date on your calendar.
  • 30-09-2020: Two invited talks were just arranged. Thanks to the invited speakers!
  • 21-09-2020: We are still working on the schedule.
  • 21-09-2020: We extended the time slots of the presentations from 20 to 30 mins (See the requirements)
  • 21-09-2020: Papers are assigned to the students according to their preferences (See the list of Topics and Material Table).
  • 22-07-2020: Registration is done via the matching system. You need to send a motivation letter with the subject "FLH_Motivatoon" to Dr. Shadi Albarqouni in order to get a higher ranking in the matching system from our side
  • 14-07-2020: Register in advance for this meeting here. After registering, you will receive a confirmation email containing information about joining the meeting.
  • 08-07-2020: Preliminary meeting: Thursday, 16.07.2020 (10:00-11:00) Friday, 17.07.2020 (11:00-12:00) in virtual meeting room (zoom).
  • 08-07-2020: Contact information-If you have any question for this seminar, please feel free to contact Dr. Shadi Albarqouni (shadi.albarqouni@tum.de)
  • 08-07-2020: 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 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 (formerly Hauptseminar), 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 (20 30 minutes presentation 1015 minutes questions).
  • 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 done in English.

Submission Deadline : You have to submit both the presentation and the blog post two weeks right after your presentation session.

Schedule

Date Session: Topic Slides Speakers
17.07.2020 (11:00 AM) Preliminary Meeting Slides  
Online Paper Assignment    
09.11.2020 @ 11:00 AM Federated Learning; Challenges, Methods, and Future I Invited Talk: The Future of Digital Health with Federated Learning Invited Speaker: Nicola Rieke from NVIDIA
16.11.2020 Federated Learning; Challenges, Methods, and Future II   Ünay, Sánchez Clemente
23.11.2020 Data Heterogeneity I   Lin, Raether
30.11.2020 Data Heterogeneity II   Stoican, Schwarz
07.12.2020 System Heterogeneity and Privacy Issues I Invited Talk: Secure, privacy-preserving and federated machine learning in medical imaging Invited Speaker: George Kaissis from Klinikum rechts der Isar
14.12.2020 System Heterogeneity and Privacy Issues II   Qian, Heidmann
21.12.2020 Data Heterogeneity III   Doerr, Arfaoui
11.01.2021 Federated Learning with Medical Imaging I   Hofmann
18.01.2021 Federated Learning with Medical Imaging II   Boysen
25.01.2021 TBA    

List of Topics and Material

The list of papers:

Topic No Title Conference/Journal Tutor Student (Last name) Link
Intro. to FL 1 FedAvg?: Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS, 2016   Ünay arXiv
  2 The Future of Digital Health with Federated Learning arXiv, 2020   Invited Speaker arXiv
Challenges 3 Federated Learning: Challenges, Methods, and Future Directions IEEE Signal Processing Magazine, 2020   Sánchez Clemente arXiv
  4 On the Convergence of FedAvg? on Non-IID Data ICLR 2020     PDF
Data Heterogeneity 5 FedMA?: Federated Learning with Matched Averaging ICLR 2020   Lin PDF
  6 Federated Adversarial Domain Adaptation ICLR 2020   Doerr PDF
  7 Federated optimization in heterogeneous networks MLSys 2020   Raether PDF
  8 FedAwS?: Federated Learning with Only Positive Labels ICML 2020     PDF
  9 SCAFFOLD: Stochastic Controlled Averaging for Federated Learning ICML 2020   Stoican PDF
  10 Federated Visual Classification with Real-World Data Distribution CVPR 2020   Schwarz PDF
System Heterogeneity 11 Federated Multi-Task Learning NeurIPS? 2017   Qian PDF
  12 Variational Federated Multi-Task Learning arXiv 2019   Heidmann arXiv
Privacy-Issues 13 Secure, privacy-preserving and federated machine learning in medical imaging Nature MI   Invited Speaker HTML
  14 Differentially Private Meta-Learning ICLR 2020     PDF
Explainability and Robustness 15 The Non-IID Data Quagmire of Decentralized Machine Learning ICML 2020   Arfaoui PDF
  16 DBA: Distributed Backdoor Attacks against Federated Learning ICLR 2020     PDF
Open Problems in FL -- Advances and Open Problems in Federated Learning arXiv -- -- PDF
Federated Learning with Medical Imaging 17 Privacy-preserving Federated Brain Tumour Segmentation MICCAIW 2019   Boysen HTML
    Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation MICCAIW 2019   Boysen HTML
  18 Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data ISBI 2019   Hofmann HTML
    Inverse Distance Aggregation for Federated Learning with Non-IID Data MICCAIW 2020   Hofmann PDF


MICCAI: Medical Image Computing and Computer Assisted Intervention
CVPR: Conference on Computer Vision and Pattern Recognition
ICML: International Conference on Machine Learning
ICLR: International Conference on Learning Representations
TMI: IEEE Transaction on Medical Imaging
JBHI: IEEE Journal of Biomedical and Health Informatics
MedIA: Medical Image Analysis (Elsevier)
TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence
BMVC: British Machine Vision Conference
MIDL: Medical Imaging with Deep Learning
NeurIPS: Neural Information Processing Systems

TeachingForm
Title: Federated Learning in Healthcare
Professor: Prof. Nassir Navab
Tutors: Dr. Shadi Albarqouni, Agnieszka Tomczak, Hasan Sarhan
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2020WiSe
Abstract: Following the great success of our on-going seminar on Deep Learning for Medical Applications, we would like to discuss advanced topics which are quite relevant to the 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.


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