Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems

Dong, C; Zhou, J; An, Q; Jiang, F; Chen, S; Pan, L; Liu, X

Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems

Dong, C Zhou, J An, Q Jiang, F

Chen, S Pan, L Liu, X

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Drones, 7, (7), pp. 413

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Field	Value	Language
dc.contributor.author	Dong, C
dc.contributor.author	Zhou, J
dc.contributor.author	An, Q
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Chen, S
dc.contributor.author	Pan, L
dc.contributor.author	Liu, X
dc.date.accessioned	2024-02-06T23:25:42Z
dc.date.available	2024-02-06T23:25:42Z
dc.identifier.citation	Drones, 7, (7), pp. 413
dc.identifier.issn	2504-446X
dc.identifier.issn	2504-446X
dc.identifier.uri	http://hdl.handle.net/10453/175401
dc.description.abstract	<jats:p>In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.</jats:p>
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Drones
dc.relation.isbasedon	10.3390/drones7070413
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Optimizing Performance in Federated Person Re-Identification through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems
dc.type	Journal Article
utslib.citation.volume	7
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-06T23:25:41Z
pubs.issue	7
pubs.publication-status	Published online
pubs.volume	7
utslib.citation.issue	7

Abstract:

In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the system’s resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.

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http://hdl.handle.net/10453/175401