Blockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection

Simple item page
dc.contributor.authorChao, Kailin
dc.contributor.authorLi, JunJie
dc.contributor.authorJiang, Yirui
dc.contributor.authorXiao, Jianmao
dc.contributor.authorCao, Yuanlong
dc.date.accessioned2025-04-14T11:09:23Z
dc.date.available2025-04-14T11:09:23Z
dc.date.freetoread2025-04-14
dc.date.issued2024-12-02
dc.date.pubOnline2025-03-24
dc.description.abstractFederated Learning with Digital Watermarks (FLDW) have been recognized as a promising solution for property protection. However, the existing FLDW-related technologies neglect the requirements of decentralized settings, leading to recurrent issues such as discrepancies in distributed client data. This paper introduces a Blockchain Federated Learning Intellectual Property Protection Framework (BFLIPR), to address the data security and model validation challenges in decentralized federated learning environments. BFLIPR merges blockchain, digital watermarking, and federated learning technologies. By harnessing the blockchain’s tamper-proof properties, digital watermarking’s concealment capabilities, and federated learning’s distributed feature, the framework offers a solution that aligns with intellectual property protection mechanism, to bolster data security and property safeguarding. Experimental findings demonstrate its high feasibility and robust for data privacy and model security in the federated learning.
dc.description.conferencename2024 IEEE Smart World Congress (SWC)
dc.description.sponsorshipThis work was supported by the National Natural Science Foundation of China under Grant No. 61962026, the Natural Science Foundation of Jiangxi Province under Grant No. 20224ACB202007, Jiangxi Provincial Natural Science Foundation under Grant No. 20224BAB212015, Jiangxi Provincial 03 Special Project, and 5G Project (20224ABC03A13, 20232ABC03A26).
dc.format.extentpp. 874-880
dc.identifier.citationChao K, Li J, Jiang Y, et al., (2024) Blockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection. In: Proceedings of the 2024 IEEE Smart World Congress (SWC), 2 - 7 December 2024, Nadi, Fiji, pp. 874-880
dc.identifier.eisbn979-8-3315-2086-1
dc.identifier.eissn2993-396X
dc.identifier.elementsID567368
dc.identifier.urihttps://doi.org/10.1109/swc62898.2024.00146
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23761
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10924967
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4604 Cybersecurity and Privacy
dc.subjectBlockchain technology
dc.subjectfederated learning
dc.subjectintellectual property protection
dc.subjectdigital watermarking
dc.subjectsmart contract
dc.subjectwatermark consensus mechanism
dc.titleBlockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection
dc.typeConference paper
dcterms.coverageNadi, Fiji
dcterms.dateAccepted2024-12-07
dcterms.temporal.endDate7 Dec 2024
dcterms.temporal.startDate2 Dec 2024

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Blockchain_and_distributed-2025.pdf
Size:
4.01 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (AIRS)