Current as of 06.04.2018, 14:30 CET

Organized by (in alphabetical order):

Jan BAUMBACH, Technical University Munich, DE
Dominik HEIDER, University of Marburg, DE
Andreas HOLZINGER, Medical University Graz, AT
Peter KIESEBERG, SBA-Research and University of Applied Sciences St.Poelten, AT
Richard ROETTGER, University of Southern Denmark, Odense, DK
Edgar WEIPPL, SBA-Research Vienna, AT


We encourage to submit original papers on novel techniques, new applications, advanced methodologies, promising research directions and discussions of unsolved future issues on, but not limited to:

  • Federated machine learning,
  • Federated learning with the human-in-the-loop,
  • Distributed learning,
  • Learning trust and reputation,
  • Client-side computing,
  • Privacy aware machine learning,
  • Privacy-by-design,
  • Privacy-by-architecture,
  • Collaborative privacy aware machine learning,
  • Blockchain security technologies,
  • Decentralized representation learning,
  • Secure feature sharing,
  • On-Device Artificial Intelligence



Increasing privacy concerns in the health domain (e.g. due to new European Data Protection Regulations) require new approaches in AI and machine learning. One problem of the health domain is, that heterogenous data sources are extremely distributed over different locations. Secure storage and sharing of sensitive health data is a big challenge and mostly prohibit open research cross-institutional, even cross-departmental. Current technologies face limitations regarding safety, security, privacy, data protection and ecosystem interoperability. Standard methods, e.g. sending sensitive health data into a cloud for analysis is meanwhile a no-go and not suitable in the future for a number of reasons. The problem is twofold: On the one hand hospitals need a secure platform to store sensitive data, but on the other hand any health research (e.g. cancer research) needs to be openly shared for global research. In the health informatics domain one possible future solution is to in federated machine learning – making use of client-side computing and latest blockchain technologies [1], [2]. The premise is NOT to share any data (!) – but to share the learned representations (features) where a lot of reserach is urgently needed in order to bring novel ideas into daily business. This approach is privacy-by-design.


This workshop brings together experts from diverse areas to pave the way for future collaborations in assessing and reducing cyber risks in hospitals and health care centers to help not only to protect sensitive patient privacy, but at the same time enable international open research on shared representations. The central goal is in improved security of health data, services and infrastructures with no risk of data privacy breaches and increased patient and researcher trust and safety in AI/machine learning approaches in open science.

All papers will be peer reviewed by at least three members of our international scientific conference committee (area 4: privacy, data protection, safety and security):

Figure above taken out of [3]
Left: Recent ERCIM special issue on Blockchain Engineering,
with two contributions from SBA-Research:
[1] Aljosha Judmayer, Alexei Zamyatin, Nicholas Stifter & Edgar Weippl 2017. Bitcoin-Cryptocurrencies and Alternative Applications. ERCIM NEWS, (110), 10-11.
[2] Nicholas Stifter, Aljosha Judmayer & Edgar Weippl 2017. A Holistic Approach to Smart Contract Security. ERCIM NEWS, (110), 17-18.

[3] 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]

For submission details please proceed to the CD-MAKE authors area