<div class="csl-bib-body">
<div class="csl-entry">Murturi, I., Donta, P. K., & Dustdar, S. (2023). <i>CommunityAI: Towards Community-based Federated Learning</i>. arXiv. https://doi.org/10.48550/arXiv.2311.17958</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/195916
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dc.description.abstract
Federated Learning (FL) has emerged as a promising paradigm to train machine learning models collaboratively while preserving data privacy. However, its widespread adoption faces several challenges, including scalability, heterogeneous data and devices, resource constraints, and security concerns. Despite its promise, FL has not been specifically adapted for community domains, primarily due to the wide-ranging differences in data types and context, devices and operational conditions, environmental factors, and stakeholders. In response to these challenges, we present a novel framework for Community-based Federated Learning called CommunityAI. CommunityAI enables participants to be organized into communities based on their shared interests, expertise, or data characteristics. Community participants collectively contribute to training and refining learning models while maintaining data and participant privacy within their respective groups. Within this paper, we discuss the conceptual architecture, system requirements, processes, and future challenges that must be solved. Finally, our goal within this paper is to present our vision regarding enabling a collaborative learning process within various communities.
en
dc.description.sponsorship
European Commission
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dc.description.sponsorship
European Commission
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dc.language.iso
en
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dc.subject
Federated Learning
en
dc.subject
Artificial Intelligence
en
dc.subject
Machine Learning
en
dc.subject
Edge-Cloud Computing
en
dc.title
CommunityAI: Towards Community-based Federated Learning
en
dc.type
Preprint
en
dc.type
Preprint
de
dc.identifier.arxiv
arXiv:2311.17958
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dc.relation.grantno
101079214
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dc.relation.grantno
101070186
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tuw.project.title
Twinning action for spreading excellence in Artificial Intelligence of Things
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tuw.project.title
Trustworthy, Energy-Aware federated DAta Lakes along the Computing Continuum
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tuw.researchTopic.id
I4
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E194-02 - Forschungsbereich Distributed Systems
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tuw.publisher.doi
10.48550/arXiv.2311.17958
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dc.description.numberOfPages
9
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tuw.author.orcid
0000-0003-0240-3834
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tuw.author.orcid
0000-0002-8233-6071
-
tuw.author.orcid
0000-0001-6872-8821
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tuw.publisher.server
arXiv
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dc.relation.ispreviousversionof
10.1109/CogMI58952.2023.00029
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.grantfulltext
none
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_816b
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item.languageiso639-1
en
-
item.openairetype
preprint
-
item.fulltext
no Fulltext
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crisitem.project.funder
European Commission
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crisitem.project.funder
European Commission
-
crisitem.project.grantno
101079214
-
crisitem.project.grantno
101070186
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.orcid
0000-0003-0240-3834
-
crisitem.author.orcid
0000-0002-8233-6071
-
crisitem.author.orcid
0000-0001-6872-8821
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering