CD-MAKE 2018 Workshop


at the International IFIP Cross  Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE in Hamburg, August 27 – 30, 2018 in conjunction with the 13th international conference on availability, reliability and security (ARES 2018),

Submissions due to April, 30, 2018
Springer LNCS camera ready deadline (hard) June, 27, 2018


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 our international scientific conference committee:


CD-MAKE is a joint effort of IFIP TC 5 (Information Technology Applications), TC 12 (Artificial Intelligence), IFIP WG 8.4 (E-Business: Multi-disciplinary research and practice), IFIP WG 8.9 (Enterprise Information Systems) and IFIP WG 12.9 (Computational Intelligence) and is held in conjunction with the International Conference on Availability, Reliability and Security (ARES).

Goal of CD-MAKE: To act as a Catalyst to bring together researchers in an cross-disciplinary manner, to stimulate fresh ideas and to encourage multi-disciplinary problem solving in the area of AI and machine learning.

CD stands for Cross-Domain and means the integration and appraisal of seemingly disparate fields (e.g. algebraic topology, entropy, geometry, etc.) and different application domains (e.g. Health, Industry 4.0, AAL, etc.) to provide an atmosphere to foster different perspectives and opinions. The conference is dedicated to offer an international platform without any boundaries for novel ideas and a fresh look on the methodologies to put crazy ideas into Business for the benefit of society. Serendipity is a desired effect, and shall cross-fertilize methodologies and transfer of algorithmic developments.

MAKE stands for MAchine Learning & Knowledge Extraction.

Written by: Heider