FLTA2023: The First International Symposium on Federated Learning Technologies and Applications Tartu, Estonia, September 18-20, 2023 |
Conference website | https://emergingtechnet.org/FLTA2023/ |
Submission link | https://easychair.org/conferences/?conf=flta2023 |
Submission deadline | June 10, 2023 |
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 FLTA 2023 aims to provide a global forum for disseminating the latest scientific research and industry results in all aspects of federated learning. FLTA 2023 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. FLCon offers an opportunity to showcase the latest advances in this area and discuss and identify future directions and challenges in FL systems. FLTA 2023 will also provide ample opportunities for networking, sharing knowledge, and collaborating with others in the metaverse community.
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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
Submission Guidelines
Paper format
Submitted papers (.pdf format) must use the A4 IEEE Manuscript Templates for Conference Proceedings. Please remember to add Keywords to your submission.
Length
There are three categories of submission.
- Long papers: 7-8 pages. Overlength papers will be rejected without review.
- Short papers: 4-6 pages.
- Poster papers: 1-2 pages (undergraduate).
Originality
Papers submitted to FLCon must be the original work of the authors. They may not be simultaneously under review elsewhere. Publications that have been peer-reviewed and have appeared at other conferences or workshops may not be submitted to FLCon. Authors should be aware that IEEE has a strict policy with regard to
plagiarism https://www.ieee.org/publications/rights/plagiarism/plagiarism-faq.html The authors’ prior work must be cited appropriately.
Author list
Please ensure that you submit your papers with the full and final list of authors in the correct order. The author list registered for each submission is not allowed to be changed in any way after the paper submission deadline.
Proofreading:
Please proofread your submission carefully. It is essential that the language use in the paper is clear and correct so that it is easily understandable. (Either US English or UK English spelling conventions are acceptable.)
Publication:
All papers that are accepted, registered, and presented in FLTA will be submitted to IEEEXplore for possible publication with the FMEC proceeding.
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Publication
FLTA2023 proceedings will be published by IEEE
Venue
The conference will be held in University of Tartu, Tartu, Estonia
Contact
All questions about submissions should be emailed to feras.awaysheh@ut.ee