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

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email: frank-mich...@fhws.de

http://promos-science.blogspot.de/

https://www.techfak.uni-bielefeld.de/~fschleif/

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