[hpc-announce] 2nd International Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC)

[Luiz F. Bittencourt]

*** CALL FOR PAPERS ***
2nd International Workshop on Distributed Machine Learning for the
Intelligent Computing Continuum (DML-ICC)
In conjunction with the 15th IEEE/ACM International Conference on
Utility and Cloud Computing (UCC 2022)
December 6-9, 2022
Portland, Oregon, USA

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, this second 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 2022 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 through Easychair:
https://easychair.org/conferences/?conf=dmlicc2022


The DML-ICC workshop invites authors to submit original and
unpublished work. Papers should not exceed 6 pages in IEEE format. Up
to two 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 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: August, 10, 2022
Notification to Authors: 15 September, 2022
Camera ready submission: 30 September, 2022
Workshop date: 6-9 December 2022



### 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 ###
(to be confirmed)

- Atakan Aral, University of Vienna, Austria
- Gabriel Antoniu, Inria, France
- Rodrigo Calheiros, Western Sydney University, Australia
- Valeria Cardellini, University of Rome Tor Vergata, Italy
- Marilia Curado, University of Coimbra, Portugal
- Ivana Dusparic, Trinity College Dublin, Ireland
- 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