Many of today's parallel machine learning algorithms were developed for tightly coupled systems like computing clusters or clouds. However, data volumes generated from machine-to-machine or human-to-machine interaction, such as mobile phones or autonomous vehicles, surpass the amount that can conveniently be centralized. Thus, traditional cloud computing approaches are rendered infeasible.
In order to scale parallel machine learning to these amounts 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 computation power of their own, for example, mobile phones, smart sensors and tablets - network communication is reduced by orders of magnitude. Moreover, it facilitates obtaining a global model without centralizing privacy-sensitive data. This novel form of parallel, distributed, and federated machine learning has gained substantial interest in recent years, both from researchers and practitioners, and may allow for disruptive changes in areas such as smart assistants, machine learning on medical or industrial data, and autonomous driving.
This workshop is the third edition of the successful DMLE workshops at ECMLPKDD 2018 and 2019. We decided to rename the workshop to broaden the scope and integrate novel directions in the field, for example, privacy-preserving federated learning in the medical domain. The workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches for parallel, distributed, and federated machine learning.
We invite participation in the 3rd Workshop on Parallel, Distributed, and Federated Learning, to be held as part of the ECMLPKDD 2020 conference. This year we invite two types of submissions to the workshop:
For all accepted papers, we invite the authors for a presentation as a poster. Moreover, for 4-6 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 2020 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.
Please note, that we do allow the submission of previously published work. However, such papers, when accepted, are excluded from the proceedings and when selecting papers for oral presentation, preference is given to original work.
We are happy to announce Peter Schlicht, Project Manager for Autonomous Driving at Volkswagen Group Research, who will give an invited talk at the workshop.
This workshop aims to provide interesting talks - both by expert invited speakers and great participants - and lively discussions. To achieve that, we have an invited talk with dedicated time for discussions. Moreover, we propose two sessions with talks for selected papers, again with time for discussions. We will issue a best paper award; the winner will receive a certificate and a prize. All accepted papers will be presented at a poster session in the end.
|09:00 - 09:10||Opening Remarks|
|09:10 - 10:00||Invited Talk|
|10:00 - 10:15||Discussion|
|10:15 - 10:30||Talks|
|10:30 - 10:50||Coffee Break|
|10:50 - 12:05||Talks|
|12:05 - 12:15||Best Paper Award|
|12:15 - 13:00||Poster Session|
Please, read about the venue in the ECMLPKDD venues web page. You will find a description of the venue and a map.
We would like to thank our amazing program committee.