Many of today's parallel machine learning algorithms were developed for tightly coupled systems like computing clusters or clouds. However, the volumes of data generated from machine-to-machine interaction, by mobile phones or autonomous vehicles, surpass the amount of data that can be realistically centralized. Thus, traditional cloud computing approaches are rendered infeasible. To scale parallel machine learning to such volumes of data, computation needs to be pushed towards the edge, that is, towards the data generating devices. By learning models directly on the data sources—which often have computational power of their own, for example, mobile phones, smart sensors, and vehicles-network communication can be reduced by orders of magnitude. Moreover, it enables raining a central model without centralizing privacy-sensitive data. This workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches for decentralized machine learning.
We invite participation in the 2nd Workshop on Decentralized Machine Learning at the Edge, to be held as part of the ECMLPKDD 2019 conference. This year we invite two types of submissions to the workshop:
For all papers, we invite the authors for a presentation as a poster. Moreover, for 4 papers, we invite the authors for a presentation as a talk during the workshop. We issue a Best Paper Award with certificate and a prize. The main topics are, including, but not limited to:
Authors should submit a PDF version in Springer LNCS style using the workshop's EasyChair site. The review process is single-blind. Proceedings shall be submitted to Springer ECML PKDD 2019 Workshop Proceedings for publication.
Full papers have a page limit of 16 pages, short papers have 8 pages, including bibliography.
Submitting a paper to the workshop means that if the paper is accepted at least one author commits to presenting it at the workshop. Papers not presented at the workshop will not be included in the proceedings.
In case you submit a paper that was rejected from ECMLPKDD, please be so kind and let us know via email. Please also indicate whether you give your consent that ECML PKDD transfers their reviews to us.