2nd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC)

Welcome

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.

Program

Important Dates

Paper submission: August 31, 2022  September 15, 2022 (Extended, hard deadline)
Notification to Authors: 13 October, 2022 (notification has been delayed, this is the new expected date)
Camera ready submission: 31 October, 2022
Workshop date: 6 December 2022
UCC Conference dates: 6-9 December 2022

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
• Distribute Machine Learning performance evaluation
• 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

Submission

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 IEEE proceedings format. Up to 2 additional pages might be purchased upon the approval of the proceedings chair. 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.

DML-ICC Workshop Co-Chairs

Program Committee