Fuzzy Federated Learning for Privacy-Preserving Detection of Adolescent Idiopathic Scoliosis

Wu, X; Ding, Y; Zhou, X; Xu, Y; Wang, S; Xu, X; Qi, L

Fuzzy Federated Learning for Privacy-Preserving Detection of Adolescent Idiopathic Scoliosis

Wu, X Ding, Y Zhou, X Xu, Y Wang, S

Xu, X Qi, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2024, PP, (99), pp. 1-15
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wu, X
dc.contributor.author	Ding, Y
dc.contributor.author	Zhou, X
dc.contributor.author	Xu, Y
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Xu, X
dc.contributor.author	Qi, L
dc.date.accessioned	2024-09-05T11:08:13Z
dc.date.available	2024-09-05T11:08:13Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2024, PP, (99), pp. 1-15
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/180720
dc.description.abstract	As a distributed intelligent paradigm, fuzzy federated learning (FuzzyFL) can reduce the uncertainty and noise of biomedical data and is suited to enhance the accurate detection of adolescent idiopathic scoliosis (AIS). The advanced paradigm requires the hospitals to share the gradient of the fuzzy deep neural network (FDNN) rather than biomedical data. Not only that, the recent research works have been devoted to privacy-preserving FuzzyFL for secure AIS detection, that adds differential privacy-based noise to the gradients against membership inference attack, attribute inference attack. However, a novel reconstruction attack called gradient leakage attack (GLA) on inferring biomedical data over the gradient brings the security challenges to FuzzyFL and thus has a negative influence on AIS detection. It is natural to ask a fundamental question: Can differentially private FuzzyFL for AIS detection over biomedical data defend GLA? In this paper, we construct a privacy-preserving FuzzyFL framework called <monospace>PrivateFuzzyFL</monospace>, that offers a great opportunity to present the systematic evaluation of the private FDNN threatened by the GLAs. In our experiments on a set of chest X-ray images and four FDNNs, we compare more than ten private fuzzy federated optimization algorithms in terms of the defense effect and the utility cost and derive that (1) The existing private FDNNs in FuzzyFL can offer a certain amount of privacy protection for biomedical data against the GLA; and (2) The perturbation algorithm with better defense effect usually causes the worse AIS detection of the FDNN.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2024.3445468
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4901 Applied mathematics
dc.title	Fuzzy Federated Learning for Privacy-Preserving Detection of Adolescent Idiopathic Scoliosis
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
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 - DSI - Data Science Institute
utslib.copyright.status	in_progress	*
dc.date.updated	2024-09-05T11:08:03Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

As a distributed intelligent paradigm, fuzzy federated learning (FuzzyFL) can reduce the uncertainty and noise of biomedical data and is suited to enhance the accurate detection of adolescent idiopathic scoliosis (AIS). The advanced paradigm requires the hospitals to share the gradient of the fuzzy deep neural network (FDNN) rather than biomedical data. Not only that, the recent research works have been devoted to privacy-preserving FuzzyFL for secure AIS detection, that adds differential privacy-based noise to the gradients against membership inference attack, attribute inference attack. However, a novel reconstruction attack called gradient leakage attack (GLA) on inferring biomedical data over the gradient brings the security challenges to FuzzyFL and thus has a negative influence on AIS detection. It is natural to ask a fundamental question: Can differentially private FuzzyFL for AIS detection over biomedical data defend GLA? In this paper, we construct a privacy-preserving FuzzyFL framework called PrivateFuzzyFL, that offers a great opportunity to present the systematic evaluation of the private FDNN threatened by the GLAs. In our experiments on a set of chest X-ray images and four FDNNs, we compare more than ten private fuzzy federated optimization algorithms in terms of the defense effect and the utility cost and derive that (1) The existing private FDNNs in FuzzyFL can offer a certain amount of privacy protection for biomedical data against the GLA; and (2) The perturbation algorithm with better defense effect usually causes the worse AIS detection of the FDNN.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180720