[hpc-announce] 1st Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC)

Luiz Fernando Bittencourt bit at ic.unicamp.br
Wed Jun 30 14:52:09 CDT 2021

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


### 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

- 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
- 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:

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.

### Important Dates ###
Paper submission: 8 August, 2021
Notification of Authors: 22 September , 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
- Luiz Bittencourt, University of Campinas, Brazil

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