Concept-Guided Interpretable Federated Learning

Yang, J; Long, G

Concept-Guided Interpretable Federated Learning

Yang, J

Long, G

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, 14472 LNAI, pp. 160-172
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Yang, J https://orcid.org/0009-0001-6420-1704
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515
dc.date.accessioned	2024-01-24T00:19:15Z
dc.date.available	2024-01-24T00:19:15Z
dc.date.issued	2024-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, 14472 LNAI, pp. 160-172
dc.identifier.isbn	9789819983902
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/174895
dc.description.abstract	Interpretable federated learning is an emerging challenge to identify explainable characteristics of each client-specific personalized model in a federated learning system. This paper proposes a novel federated concept bottleneck (FedCBM) method by introducing human-friendly concepts for client-wise model interpretation. Specifically, given a set of pre-defined concepts, all clients will collaboratively train shared Concept Activation Vectors (CAVs) in federated settings. The shared concepts will be the information carrier to align client-specific representations, and also be applied to enhance the model’s accuracy under a supervised learning loss. The effectiveness of our method and concept-level reasoning is demonstrated in our experimental analysis.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-981-99-8391-9_13
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Concept-Guided Interpretable Federated Learning
dc.type	Conference Proceeding
utslib.citation.volume	14472 LNAI
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-24T00:19:14Z
pubs.publication-status	Published
pubs.volume	14472 LNAI

Abstract:

Interpretable federated learning is an emerging challenge to identify explainable characteristics of each client-specific personalized model in a federated learning system. This paper proposes a novel federated concept bottleneck (FedCBM) method by introducing human-friendly concepts for client-wise model interpretation. Specifically, given a set of pre-defined concepts, all clients will collaboratively train shared Concept Activation Vectors (CAVs) in federated settings. The shared concepts will be the information carrier to align client-specific representations, and also be applied to enhance the model’s accuracy under a supervised learning loss. The effectiveness of our method and concept-level reasoning is demonstrated in our experimental analysis.

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http://hdl.handle.net/10453/174895