In conjuction with
IEEE/ACM UCC 2024
December 16-19,2024 - Sharjah, UAE
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 previous editions DML-ICC 2021, DML-ICC 2022, and DML-ICC 2023, this fourth 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.
Paper submission: August 31, 2024 September 27, 2024 (Extended, hard deadline)
Notification to Authors: October 15, 2024 (Updated)
Camera ready submission: November 10, 2024 (updated - further instructions will be sent to authors by e-mail)
Workshop date: TBD
UCC Conference dates: 16-19 December 2024
DML-ICC 2024 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:
• Business and cost models enabled by distributed learning
• Distributed Machine Learning for Resource Management and Scheduling
• Distributed Machine Learning in the Computing Continuum
• Distributed Machine Learning applications
• Distributed learning in Complex Event Processing and Stream Processing
• Distributed Machine Learning performance evaluation
• Edge Intelligence models and architectures
• Federated Learning
• Intelligent Computing Continuum architectures and models
• Intelligent/ML management for Autonomic Computing in the Continuum
• Intelligent/ML management for Network Slicing for the Continuum
• 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
Submission link through EasyChair: click here
Paper submission is double-blind electronic only through EasyChair. The DML-ICC workshop invites authors to submit original and unpublished work. Papers should not exceed 6 pages in IEEE 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. The submitted document should not include author information and should not include acknowledgements, citations or discussion of related work that would make the authorship apparent. Submissions containing author identifying information may be subject to rejection without review.
Submission requires the willingness of at least one of the authors to register as author, non-student rate and present the paper in person.
At least one author of each paper must be registered for the conference for the paper to be published in the proceedings. The conference proceedings will be published by the IEEE and made available online via the IEEE Xplore Digital Library and ACM Digital Library.
Ian Foster, University of Chicago and Argonne National Laboratory, USA
Filip De Turck, Ghent University, Belgium
Luiz F. Bittencourt, Universidade Estadual de Campinas, Brazil
Valeria Cardellini, University of Rome Tor-Vergata, Italy
Yassine Himeur, University of Dubai, UAE
Atakan Aral, University of Vienna, Austria
Marios Avgeris, University of Amsterdam, Netherlands
José Javier Berrocal-Olmeda, University of Extremadura, Spain
Bruno Casella, University of Torino, Italy
Rodrigo Calheiros, Western Sydney University, Australia
Marilia Curado, University of Coimbra, Portugal
Ivana Dusparic, Trinity College Dublin, Ireland
Omar Elharrouss, UAE University, UAE
Fodil Fadli, Qatar University, Qatar
Mohammadreza Hoseinyfarahabady, University of Sydney, Australia
Stefano Iannucci, University of Rome III, Italy
Carlos Kamienski, Federal University of ABC, Brazil
Wei Li, University of Sydney, Australia
Zoltán Mann, University of Amsterdam, Netherlands
Wathiq Mansoor, University of Dubai, UAE
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
Iraklis Varlamis, Harokopio University of Athens, Greece
Karima Velasquez, University of Coimbra, Portugal