SoK: Private Knowledge Sharing in Distributed Learning

Supeksala, Y; Ranbaduge, T; Ding, M; Nguyen, DC; Liu, B; Chua, C; Zhang, J

SoK: Private Knowledge Sharing in Distributed Learning

Supeksala, Y Ranbaduge, T Ding, M Nguyen, DC Liu, B

Chua, C Zhang, J

Permalink

Publisher:: Privacy Enhancing Technologies Symposium Advisory Board
Publication Type:: Journal Article
Citation:: Proceedings on Privacy Enhancing Technologies, 2025, (4), pp. 485-506

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (817.71 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Supeksala, Y
dc.contributor.author	Ranbaduge, T
dc.contributor.author	Ding, M
dc.contributor.author	Nguyen, DC
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Chua, C
dc.contributor.author	Zhang, J
dc.date.accessioned	2026-05-20T23:52:26Z
dc.date.available	2026-05-20T23:52:26Z
dc.identifier.citation	Proceedings on Privacy Enhancing Technologies, 2025, (4), pp. 485-506
dc.identifier.issn	2299-0984
dc.identifier.issn	2299-0984
dc.identifier.uri	http://hdl.handle.net/10453/195105
dc.description.abstract	The rapid advancement of Artificial Intelligence (AI) has transformed various industries, leading to the widespread distribution of AI models and data across intelligent systems. As modern data driven services increasingly integrate distributed knowledge entities, decentralized learning has become a prevalent approach to training AI models. However, this collaborative learning paradigm introduces significant security vulnerabilities and privacy challenges. This paper presents a comprehensive systematic review on private knowledge sharing in distributed learning, analyzing key knowledge components utilized in leading distributed learning architectures. We identify critical vulnerabilities associated with these components and examine defensive strategies to safeguard privacy while mitigating potential adversarial threats. Additionally, we highlight key limitations in knowledge sharing in distributed learning and propose future research directions to enhance security and efficiency in decentralized AI systems.
dc.language	en
dc.publisher	Privacy Enhancing Technologies Symposium Advisory Board
dc.relation.ispartof	Proceedings on Privacy Enhancing Technologies
dc.relation.isbasedon	10.56553/popets-2025-0141
dc.rights	info:eu-repo/semantics/openAccess
dc.title	SoK: Private Knowledge Sharing in Distributed Learning
dc.type	Journal Article
utslib.citation.volume	2025
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-05-20T23:52:23Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	2025
utslib.citation.issue	4

Abstract:

The rapid advancement of Artificial Intelligence (AI) has transformed various industries, leading to the widespread distribution of AI models and data across intelligent systems. As modern data driven services increasingly integrate distributed knowledge entities, decentralized learning has become a prevalent approach to training AI models. However, this collaborative learning paradigm introduces significant security vulnerabilities and privacy challenges. This paper presents a comprehensive systematic review on private knowledge sharing in distributed learning, analyzing key knowledge components utilized in leading distributed learning architectures. We identify critical vulnerabilities associated with these components and examine defensive strategies to safeguard privacy while mitigating potential adversarial threats. Additionally, we highlight key limitations in knowledge sharing in distributed learning and propose future research directions to enhance security and efficiency in decentralized AI systems.

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

http://hdl.handle.net/10453/195105