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*Call for Papers*
Federated Learning Applications in the Real World (FLARW 2024
<https://www.fvv.um.si/eicc2024/flarw/>) to be held in conjunction with
the European Interdisciplinary Cybersecurity Conference (EICC 2024
<https://www.fvv.um.si/eicc2024/>) 5-6 June, 2024 in Xanthi, Greece.
https://www.fvv.um.si/eicc2024/flarw/
Following a data protection by design principle, federated learning has
emerged as a more privacy-friendly machine learning paradigm for
independent actors to collaboratively train a machine learning model
without sharing their local training data with a central server or other
nodes. When applying federated learning in real-world scenarios,
especially where there are complicated requirements on protection of
local data (e.g., in health and crime-related data), a wide range of
socio-technical aspects have to be considered together with technical
ones. For instance, when federated learning is used to facilitate health
data sharing between public hospitals and private companies, the system
designers and implementers have to consider various aspects related to
business models, legal compliance, regulatory processes, economic
factors, human behaviours, and ethics, e.g., how relevant stakeholders
especially patients and carers can be involved to ensure the
transparency of the whole process and their actual implementations, how
non-expert users perceive such technical solutions and adopt them, how
complicated patient consents can be managed, how different subsets of
data can be anonymised but remain linkable to allow more useful health
analytics, and what ethical issues should be considered to better manage
conflicting interests of different parties. Considering such
socio-technical aspects in developing, deploying and evaluating
federated learning to maintain real-world security and privacy
requirements is not trivial, and often introduces complicated trade-offs
that designers, developers and practitioners have to consider carefully.
This special session aims at providing a platform for researchers from
different disciplines to share their latest research work on security
and privacy aspects of practical applications of federated learning in
the real world, with some interdisciplinary elements on one or more
relevant socio-technical elements. We particularly welcome researchers
from outside of Computer Science and Electronic Engineering to submit
their work.
The list of possible topics includes, but is not limited to:
* Security or privacy-critical applications of federated learning in
different domains (e.g., health, energy, finance, policing,
e-government)
* Human factors in federated learning applications (e.g., usability,
attitude towards adoption)
* Behavioural aspects of real-world federated learning systems
* Legal aspects of real-world federated learning systems
* New business models enabled by federated learning
* Economic aspects of federated learning (e.g., incentivisation,
economic modelling)
* Ethical considerations related to the use of federated learning
* Real-world challenges of deploying federated learning and solutions
* Societal impacts of federated learning in real-world applications
* Socio-technical aspects of attacks on federated learning
* Privacy risks or attacks of federated learning in real-world
applications and solutions
Important Dates:
📅 Submission Deadline: March 1, 2024
📅 Author Notification: April 15, 2024
📅 Camera-Ready Deadline: April 26, 2024
The proceedings of the special session will be published by ACM.
Special Session co-chairs:
- Pavlos S. Efraimidis, Democritus University of Thrace, Greece
- Shujun Li, University of Kent, UK
Learn more and submit your papers at: https://www.fvv.um.si/eicc2024/flarw/
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