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DML-ICC 2021 : 1st International Workshop on Distributed Machine Learning for the Intelligent Computing Continuum | |||||||||||||||
Link: http://www.lrc.ic.unicamp.br/dml-icc/ | |||||||||||||||
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Call For Papers | |||||||||||||||
*** CALL FOR PAPERS ***
1st International Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC) In conjunction with the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2021) December 6-9, 2021 Leicester, UK http://www.lrc.ic.unicamp.br/dml-icc/ ### Background ### As the cloud extends to the fog and to the edge, computing services can be scattered over a set of computing resources that encompass users’ devices, the cloud, and intermediate computing infrastructure deployed in between. Moreover, increasing networking capacity promises lower delays in data transfers, enabling a continuum of computing capacity that can be used to process large amounts of data with reduced response times. Such large amounts of data are frequently processed through machine learning approaches, seeking to extract knowledge from raw data generated and consumed by a widely heterogeneous set of applications. Distributed machine learning has been evolving as a tool to run learning tasks also at the edge, often immediately after the data is produced, instead of transferring data to the centralized cloud for later aggregation and processing. The DML-ICC workshop aims to be a forum for discussion among researchers with a distributed machine learning background and researchers from parallel/distributed systems and computer networks. By bringing together these research topics, we look forward in building an Intelligent Computing Continuum, where distributed machine learning models can seamlessly run on any device from the edge to the cloud, creating a distributed computing system that is able to fulfill highly heterogeneous applications requirements and build knowledge from data generated by these applications. ### Topics ### DML-ICC 2021 workshop aims to attract researchers from the machine learning community, especially the ones involved with distributed machine learning techniques, and researchers from the parallel/distributed computing communities. Together, these researchers will be able to build resource management mechanisms that are able to fulfill machine learning jobs requirements, but also use machine learning techniques to improve resource management in large distributed systems. Topics of interest include but are not limited to: - Autonomic Computing in the Continuum - Business and Cost Models for the Computing Continuum - Complex Event Processing and Stream Processing - Computing and Networking Slicing for the Continuum - Distributed Machine Learning for Resource Management and Scheduling - Distributed Machine Learning in the Computing Continuum - Distributed Machine Learning applications - Distribute Machine Learning performance evaluation - Edge Intelligence models and architectures - Edge Intelligence models and architectures - Federated Learning - Intelligent Computing Continuum architectures and models - Management of Distributed Learning Tasks - Mobility support in the Computing Continuum - Network management in the Computing Continuum - Privacy using Distributed Learning - Programming models for the Computing Continuum - Resource management and Scheduling in the computing continuum - Smart Environments (Smart Cities, Smart Buildings, Smart Industry, etc.) - Theoretical Modeling for the Computing Continuum ### Submissions ### Paper submission is through Easychair: https://easychair.org/conferences/?conf=dmlicc2021 The DML-ICC workshop invites authors to submit original and unpublished work. Papers should not exceed 6 pages in ACM format. Additional pages might be purchased upon the approval of the proceedings chair. All selected papers for this workshop are peer-reviewed and will be published in IEEE Xplore and ACM Portal. Submission requires the willingness of at least one of the authors to register and present the paper. Please check the DML-ICC webpage for more details on paper format: http://www.lrc.ic.unicamp.br/dml-icc/ ### Important Dates ### Paper submission: 01 October, 2021 (Extended, hard deadline) Notification to Authors: 23 October , 2021 Camera ready submission: 31 October, 2021 Workshop date: Exact date to be determined (6-9 December 2021) ### Workshop Chairs ### - Ian Foster, University of Chicago and Argonne National Laboratory, USA - Filip De Turck, Ghent University, Belgium - Luiz Bittencourt, University of Campinas, Brazil ### Program Committee (preliminary - to be updated) ### - Gabriel Antoniu, Inria, France - Rodrigo Calheiros, Western Sydney University, Australia - Roch Glitho, Concordia University, Canada - Mohammadreza Hoseinyfarahabady, University of Sydney, Australia - Carlos Kamienski, Federal University of ABC, Brazil - Wei Li, University of Sydney, Australia - Zoltán Mann, University of Duisburg-Essen, Germany - Radu Prodan, University of Klagenfurt, Austria - Omer Rana, Cardiff University, United Kingdom - Christian Esteve Rothenberg, University of Campinas, Brazil - Rizos Sakellariou, University of Manchester, United Kingdom - Josef Spillner, Zurich University of Applied Sciences, Switzerland - Javid Taheri, Karlstad University, Sweden - Massimo Villari, University of Messina, Italy |
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