[Apologies if you got multiple copies of this invitation]

The International Symposium on Federated Learning Technologies and Applications (FLTA 2023)

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