Workshop

Secure Federated Machine Learning for Health Informatics

Workshop-Date: Thursday, March, 1, 2018 and Friday, March, 2, 2018
Welcome Reception: Wednesday, February, 28, 2018, 20:00
Venue: SBA-research, Favoritenstrasse 16, 1040 Vienna, Austria
Direction: Metro line U1 (red line) > station Taubstummengasse > exit Floragasse
Local Organizers: Andreas HOLZINGER, Bernd MALLE & Peter KIESEBERG
Current as of 18.01.2018 12:00 CET

Scientific Workshop Committee:
Prof. Dr. Jan BAUMBACH, Baumbachlab, Experimental Bioinformatics Lab, Technical University Munich, DE
Prof. Dr. Dominik HEIDER, Heiderlab, Department of Mathematics and Computer Science, University of Marburg, DE
Prof. Dr. Andreas HOLZINGER, Holzinger Group HCI-KDD, Inst. for Med. Informatics/Statistics, Medical University Graz, AT
Prof. Dr. Richard ROETTGER, Pract. Computer Science/Bioinformatics Group, University of Southern Denmark, Odense, DK
Prof. Dr. Edgar WEIPPL, SBA-Research Vienna, AT

Scientific Advisory Board:

Increasing privacy concerns in the health domain (e.g. due to the new European Data Protection Regulation) call for new approaches in machine learning. One problem is that in the health domain the data sources are extremely distributed over different locations. Standard methods, e.g. sending data into a cloud for analysis is meanwhile a no-go and not suitable in the future for a number of reasons. On the other hand hospitals need a secure platform to store senisitive data, but on the other hand biomedical research needs to share and excchange these data. In the health domain one possible future solution is to make use of federated machine learning – but here we need a lot of reserach to bring this idea into Business. This workshop brings together interested researchers and paves the way for future collaborations. Please note that this workshop is by invitation only – if you have interest please contact the organizers.

Figure taken out of [1]

[1] Bernd Malle, Nicola Giuliani, Peter Kieseberg & Andreas Holzinger 2017. The More the Merrier – Federated Learning from Local Sphere Recommendations. Machine Learning and Knowledge Extraction, IFIP CD-MAKE, Lecture Notes in Computer Science LNCS 10410 Cham: Springer, pp. 367-374. [preprint] [doi: 10.1007/978-3-319-66808-6_24]

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