Seminar on

Federated Learning in Healthcare

Shadi Albarqouni, PhD

AI Young Investigator Group Leader at Helmholtz AI | TUM Junior Fellow at TU Munich

Content

• Brief introduction about Federated Learning
• It is the right thing at the right time!
• Course structure
• Registration

Deep Learning success comes at the cost of collecting and processing a massive amount of data , which often are not accessible due to privacy issues .

Federated Learning has been recently introduced to allow training DL models without sharing the data .

_{Taken from Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H.R., Albarqouni, S., Bakas, S., Galtier, M.N., Landman, B.A., Maier-Hein, K. and Ourselin, S., 2020. The future of digital health with federated learning. NPJ digital medicine, 3(1), pp.1-7.} 

The principal challenges , to overcome, concern the nature of medical data, namely

• **Data heterogeneity**; severe class-imbalance, few amounts of annotated data, inter-/intra-scanners variability (domain shift), and inter-/intra-observer variability (noisy annotations)

Right thing at the right time

Course Structure

Basic Info.

• Type: Master Seminar (IN2107)
• Language: English
• SWS: 2
• ECTS: 5
• Webpage: https://albarqouni.github.io/courses/flhsose2021/
• Time: Fridays, 10 - 12
• Location: Virtual Event
• Requirements: Solid Background in Machine/Deep Learning

Tutors

Objectives: Learn through read, understand, present, and discuss many scientific papers¹ tackling the challenges present in Federated Learning.

_{¹Our pool of papers includes the ones published in NeurIPS, ICML, ICLR, IEEE TMI, MedIA, MICCAI, MIDL, and ISBI.}

Schedule:

Evaluation:

• **Presentation (60%)**: The selected paper is presented to the other participants (30 minutes presentation plus 10 minutes Q&A)

Insights into the course evaluation?

If you are interested in this seminsr course, please write a brief motivation paragraph (few lines) showing your interest and your background in Machine/Deep Learning and send it with a subject “FLH_Motivation”, to Shadi Albarqouni ( shadi.albarqouni@tum.de). Deadline is 16.02.2021.

Questions?