[hpc-announce] Distributed Machine Learning for the Computing Continuum - DML-ICC @ IEEE/ACM UCC - Extended deadline

[Luiz F. Bittencourt]

*** CALL FOR PAPERS ***
3rd International Workshop on Distributed Machine Learning for the
Intelligent Computing Continuum (DML-ICC)
In conjunction with the 16th IEEE/ACM International Conference on Utility
and Cloud Computing (UCC 2023)
December 4-7, 2023
Taormina (Messina), Italy

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.

Following the successful DML-ICC 2021 and DML-ICC 2022, this third edition
of DML-ICC keeps the aim 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 2023 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
- Distributed Machine Learning performance evaluation
- 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 double-blind, through Easychair:
https://easychair.org/conferences/?conf=dmlicc2023

Paper submission is electronic only. Authors should use the Easychair
system. The DML-ICC workshop invites authors to submit original and
unpublished work. Papers should not exceed 6 pages in ACM double-column
format, including figures, tables, and references. Up to 2 additional pages
might be purchased upon the approval of the proceedings chair.

All manuscripts will undergo a double-blind review process and will be
reviewed and judged on correctness, originality,technical strength, rigour
in analysis, quality of results, quality of presentation, and interest and
relevance to the conference attendees. Your submission is subject to a
determination that you are not under any sanctions by ACM.

At least one author of each paper must be registered for the conference in
order for the paper to be published in the proceedings. The conference
proceedings will be published by the ACM and made available online via the
IEEE Xplore Digital Library and ACM Digital Library.

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 due:  21 September, 2023 (EXTENDED)
Notification of acceptance: 15 October, 2023 (updated)
Final camera-ready papers due: 31 October, 2023 (updated)
Workshop date: 4-7 December 2023


### DML-ICC Honorary Chairs ###
- Ian Foster, University of Chicago and Argonne National Laboratory, USA
- Filip De Turck, Ghent University, Belgium

### DML-ICC Co-Chairs
- Marco Aldinucci, University of Torino, Italy
- Luiz F. Bittencourt, University of Campinas, Brazil
- Valeria Cardellini, University of Rome Tor-Vergata, Italy


### Program Committee ##
- Atakan Aral, University of Vienna, Austria
- José Javier Berrocal-Olmeda, University of Extremadura, Spain
- Robert Birke, University of Torino, Italy
- Rodrigo Calheiros, Western Sydney University, Australia
- Bruno Casella, University of Torino, Italy
- Marilia Curado, University of Coimbra, Portugal
- Bruno Casella, University of Torino, Italy
- Ivana Dusparic, Trinity College Dublin, Ireland
- Stefano Iannucci, University of Rome III, Italy
- Carlos Kamienski, Federal University of ABC, Brazil
- Wei Li, University of Sydney, Australia
- Zoltán Mann, University of Duisburg-Essen, Germany
- Gianluca Mittone, University of Torino, Italy
- Radu Prodan, University of Klagenfurt, Austria
- Christian Esteve Rothenberg, University of Campinas, Brazil
- Josef Spillner, Zurich University of Applied Sciences, Switzerland
- Javid Taheri, Karlstad University, Sweden
- Karima Velasquez, University of Coimbra, Portugal