Connectionists: ESANN 2021 SS CFP - Federated Learning – Methods, Applications and Beyond

[Frank-Michael Schleif]

-- Apologies in advance for multiple postings --
                        Call for Papers
                   Special Session on
'Federated Learning – Methods, Applications and Beyond'
          06-08 October 2021, Bruges, Belgium
                 https://www.esann.org/

AIMS AND SCOPE
In today’s world, important data is often distributed over different
facilities or devices. E. g. in the context of the internet of things,
smart devices, or the health care domain. In the latter one, different
hospitals and private doctors, will hold different information of the
same patient and models can be derived in a federated learning scheme
to avoid data transmissions and to ensure
privacy constraints. Just collecting data from all facilities/devices
as it is, is mostly forbidden due to data privacy reasons. One
direction to address this issue is given by means of differential
privacy concepts. Another problem is, that in some scenarios, the data
in some of the facilities are changing rapidly,
and thus updated data would need to be sent to the model very
frequently. Thus, minimizing communication effort is also a challenge.
To address this issue, there currently exist different algorithms of
the Federated Learning domain, like Federated Averaging (FedAvg),
which address
these problems. This special session welcomes novel research and
applications in the field of Federated Learning and beyond. Especially
addressing the challenges of expensive communication, systems
heterogeneity, statistical heterogeneity, and privacy concerns.

TOPICS
We encourage the submission of papers on novel Federated Learning
methods by means of computational intelligence and machine learning
approaches, including but not limited to:

- data analysis and pattern recognition approaches for federated environments
- preprocessing approaches in collaborative learning
- learning in heterogeneous systems
- effective communication for updating distributed models
- representation and modelling of distributed models
- approximation techniques for Federated Learning
- online and incremental learning (dimensionality reduction,
classification, clustering and
regression, outlier detection) with a particular design for federated
environments
- Federated Transfer Learning algorithms
- Vertical Federated Learning algorithms
- Horizontal Federated Learning algorithms
- security and privacy preservation in federated environments
- differential privacy techniques
- model compression and adaptive model aggregation
- application of Deep Learning in the Federated Learning context
- particular interesting applications for Federated Learning e.g. in
IoT, recommendation systems,
medicine, sensor networks, text processing...

SUBMISSION:
Prospective authors must submit their paper through the ESANN portal
following the instructions provided in https://www.esann.org/node/6  Each
paper will undergo a peer reviewing process for its acceptance.

IMPORTANT DATES:
• Paper submission deadline: 10 May 2021
• Notification of acceptance: 20 July 2021
• Deadline for final papers: 20 August 2021
• The ESANN 2021 conference: 06-08 October 2021

SPECIAL SESSION ORGANIZERS:
• Frank-Michael Schleif, University of Birmingham, Birmingham, UK
• Fabrice Rossi, CEREMADE, University Paris Dauphine PSL
• Christoph Raab, University of Bielefeld, Germany
• Moritz Heusinger, University of Appl. Sc. Wuerzburg-Schweinfurt, Germany

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Prof. Dr. rer. nat. habil. Frank-Michael Schleif
School of Computer Science
University of Applied Sciences Würzburg-Schweinfurt
Sanderheinrichsleitenweg 20
Raum I-3.35
Tel.: +49(0) 931 351 18127
97074 Würzburg

Honorable Research Fellow
The University of Birmingham
Edgbaston
Birmingham B15 2TT
United Kingdom
-
email: frank-michael.schleif at fhws.de
http://promos-science.blogspot.de/
https://www.techfak.uni-bielefeld.de/~fschleif/
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