<div class="csl-bib-body">
<div class="csl-entry">Li, Y., Wang, X., Zeng, R., Donta, P. K., Murturi, I., Huang, M., & Dustdar, S. (2023). <i>Federated Domain Generalization: A Survey</i>. arXiv. https://doi.org/10.34726/5945</div>
</div>
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/195922
-
dc.identifier.uri
https://doi.org/10.34726/5945
-
dc.description.abstract
Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future.
en
dc.description.sponsorship
European Commission
-
dc.language.iso
en
-
dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
-
dc.subject
Domain shift
en
dc.subject
domain generalization
en
dc.subject
privacy preserving
en
dc.subject
federated domain generalization
en
dc.subject
machine learning
en
dc.title
Federated Domain Generalization: A Survey
en
dc.type
Preprint
en
dc.type
Preprint
de
dc.rights.license
Urheberrechtsschutz
de
dc.rights.license
In Copyright
en
dc.identifier.doi
10.34726/5945
-
dc.identifier.arxiv
2306.01334
-
dc.contributor.affiliation
Northeastern University, China
-
dc.contributor.affiliation
Northeastern University, China
-
dc.contributor.affiliation
Northeastern University, China
-
dc.contributor.affiliation
Northeastern University, China
-
dc.relation.grantno
101070186
-
tuw.project.title
Trustworthy, Energy-Aware federated DAta Lakes along the Computing Continuum
-
tuw.researchTopic.id
I4
-
tuw.researchTopic.name
Information Systems Engineering
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E194-02 - Forschungsbereich Distributed Systems
-
tuw.publisher.doi
10.48550/arXiv.2306.01334
-
dc.description.numberOfPages
39
-
tuw.author.orcid
0000-0002-6585-0714
-
tuw.author.orcid
0000-0003-2856-4716
-
tuw.author.orcid
0000-0002-8233-6071
-
tuw.author.orcid
0000-0003-0240-3834
-
tuw.author.orcid
0000-0003-3793-968X
-
tuw.author.orcid
0000-0001-6872-8821
-
dc.rights.identifier
Urheberrechtsschutz
de
dc.rights.identifier
In Copyright
en
dc.description.sponsorshipexternal
National Natural Science Foundation of China
-
dc.description.sponsorshipexternal
National Natural Science Foundation of China
-
dc.description.sponsorshipexternal
European Commission
-
dc.relation.grantnoexternal
92267206
-
dc.relation.grantnoexternal
62032013
-
dc.relation.grantnoexternal
101135576
-
tuw.publisher.server
arXiv
-
wb.sciencebranch
Informatik
-
wb.sciencebranch.oefos
1020
-
wb.sciencebranch.value
100
-
item.grantfulltext
open
-
item.openairetype
preprint
-
item.openaccessfulltext
Open Access
-
item.openairecristype
http://purl.org/coar/resource_type/c_816b
-
item.languageiso639-1
en
-
item.fulltext
with Fulltext
-
item.mimetype
application/pdf
-
item.cerifentitytype
Publications
-
crisitem.author.dept
Northeastern University, China
-
crisitem.author.dept
Northeastern University, China
-
crisitem.author.dept
Northeastern University, China
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
Northeastern University, China
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.orcid
0000-0003-2856-4716
-
crisitem.author.orcid
0000-0002-8233-6071
-
crisitem.author.orcid
0000-0003-0240-3834
-
crisitem.author.orcid
0000-0003-3793-968X
-
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