<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class="">All,</div><div class=""><br class=""></div><div class="">One of our former Auton Lab members Yamuna Krishnamurthy is co-chairing a workshop on Decentralized Machine Learning at ECML 2018. In case you are interested, here is the call for papers:</div><div class=""><br class=""></div><div class=""><br class=""></div>Decentralized Machine Learning at the Edge (DMLE'18)<br class="">_____________________________________________<br class=""> <br class="">Call for Papers <br class="">Website: dmle.iais.fraunhofer.de<br class="">Workshop in conjunction with ECMLPKDD 2018<br class="">Sep 14, 2018, Dublin, Ireland<br class=""><br class="">This workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches for decentralized machine learning. In order to scale parallel machine learning to very large volumes of data, decentralized machine learning pushes computation towards the edge, that is, towards the data generating devices. By learning models directly on the data sources, network communication can be reduced by orders of magnitude. Moreover, it enables training a central model without centralizing privacy-sensitive data.<br class=""><br class="">Submission deadline is July 2nd 2018<br class=""><br class="">We invite submissions of full length (16 pages) and short (8 pages) papers. In addition, we will present a best paper award which includes a certificate and prize:<br class=""> • Parallel machine learning<br class=""> • Edge computing for machine learning<br class=""> • Decentralized deep learning<br class=""> • Federated learning<br class=""> • In-situ methods<br class=""> • Communication-efficient learning<br class=""> • Privacy aspects of distributed learning<br class=""> • Black-box machine learning<br class=""> • Distributed optimization<br class=""> • Theoretical investigations on parallelization<br class=""> • Large-scale machine learning, massive data sets<br class=""> • Distributed data mining<br class=""><div class="">
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