The International Symposium on Federated Learning Technologies and
Applications (FLTA 2023)
https://emergingtechnet.org/FLTA2023/
In Conjunction with
The Eighth International Conference on Fog and Mobile Edge Computing (FMEC
2023)
emergingtechnet.org/FMEC2022/index.php
Tartu, Estonia. September 18-20, 2023
Technically Co-Sponsored by IEEE Estonia Section
*FLTA 2023 CFP:*
We live in a data-driven era where AI and ML are integrated into every
aspect of life and industry when making decisions. Recent AI/ML
applications, scenarios, and use cases data sources come from large-scale
distributed and diverse sources, i.e., in terms of capacity and data
heterogeneity. Such an approach empowers applications to discover unique
insights, which can be intelligently utilized to provide better services
and user experience. Yet it imposes serious debate on data and client
privacy, specifically on data protection regulations and restrictions such
as EU GDPR. Moreover, collecting, aggregating, and integrating
heterogeneous data dispersed over various data sources and securely
managing and processing the data are non-trivial tasks. The challenges are
not only due to transporting high-volume, high-velocity, high-veracity,
cybersecurity attacks, and heterogeneous data across organizations. There
is also a challenge with domain-specific language models to get enough
training data since it is usually private or sensitive, with complicated
administrative procedures surrounding it. Such private data include users'
financial transactions, patients' health data, or camera footage on the
street. In this context, Federated learning (FL) has emerged as a
prospective solution that facilitates distributed collaborative learning
without disclosing original training data. The idea behind FL is to train
the ML model collaboratively among distributed actors without sharing their
data and violating the privacy accord. FL locates ML services and
operations closer to the clients, facilitating leveraging available
resources on the network's edge. Hence, FL has become a critical enabling
technology for future intelligent applications in domains such as
autonomous driving, smart manufacturing, and healthcare. This development
will lead to an overall advancement of FL and its impact on the community,
noting that FL has gained significant attention within the machine learning
community in recent years.
The FLTA2023 aims to provide a global forum for disseminating the latest
scientific research and industry results in all aspects of federated
learning. FLTA2023 also aims to bring together researchers, practitioners,
and edge intelligence advocators in sharing and presenting their
perspectives on the effective management of FL deployment architectures.
The symposium will address the theoretical foundations of the field, as
well as applications, datasets, benchmarking, software, hardware, and
systems. Also, to create an annual forum for researchers and practitioners
who share an interest in FL. FLTA offers an opportunity to showcase the
latest advances in this area and discuss and identify future directions and
challenges in FL systems. FLCon2023 will also provide ample opportunities
for networking, sharing knowledge, and collaborating with others in the
metaverse community.
*Topics of interest:*
- Federated Learning frameworks
- Federated Learning Aggregation Algorithms
- Federated Learning Applications
- Federated Learning Deployment Architectures
- Privacy-Preserving FL Techniques
- Federated Learning Communication-efficiency
- Federated Learning modelling and simulation tools
- Federated Learning datasets and benchmarking
- Federated Learning Associated Technologies
*Submissions Guidelines and Proceedings*
Manuscripts should be prepared in 10-point font using the IEEE 8.5" x 11"
two-column format. All papers should be in PDF format, and submitted
electronically at Paper Submission Link. A full paper can be up to 8 pages
(including all figures, tables and references). Submitted papers must
present original unpublished research that is not currently under review
for any other conference or journal. Papers not following these guidelines
may be rejected without review. Also submissions received after the due
date, exceeding length limit, or not appropriately structured may also not
be considered. Authors may contact the Program Chair for further
information or clarification. All submissions are peer-reviewed by at least
three reviewers. Accepted papers will appear in the FMEC Proceeding, and be
published by the IEEE Computer Society Conference Publishing Services and
be submitted to IEEE Xplore for inclusion.
Submitted papers must include original work, and must not be under
consideration for another conference or journal. Submission of regular
papers up to 8 pages and must follow the IEEE paper format. Please include
up to 7 keywords, complete postal and e-mail address, and fax and phone
numbers of the corresponding author. Authors of accepted papers are
expected to present their work at the conference. Submitted papers that are
deemed of good quality but that could not be accepted as regular papers
will be accepted as short papers. Length of short papers can be between 4
to 6 pages.
*Important Dates*
Full Paper Submission Date: 30 May 2023
Short paper/poster due: June 10, 2023
Notification to Authors: 30 July 2023
Camera Ready Submission: 15 August 2023
*Contact:*
Please send any inquiry on FLTA to Feras Awaysheh: feras.awaysheh@ut.ee
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