No Cover Image

Journal article 25 views 5 downloads

Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework

Fatima Tariq Orcid Logo, Fatima Anjum, Cheng Cheng Orcid Logo, Shazia Javed Orcid Logo, Khursheed Aurangzeb, Nadia Kanwal

PLOS One, Volume: 21, Issue: 3, Start page: e0342454

Swansea University Author: Cheng Cheng Orcid Logo

  • 71610.VOR.pdf

    PDF | Version of Record

    © 2026 Tariq et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.

    Download (8.64MB)

Check full text

DOI (Published version): 10.1371/journal.pone.0342454

Abstract

In smart grids, data collection is carried out through smart meters and devices of the Internet of Things, which are installed in the home, allowing to predict the demand for electricity and optimize the distribution of energy. Although the smart grids improve efficiency of operations for end users,...

Full description

Published in: PLOS One
ISSN: 1932-6203
Published: Public Library of Science (PLoS) 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa71610
first_indexed 2026-03-11T10:31:45Z
last_indexed 2026-03-12T05:33:38Z
id cronfa71610
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2026-03-11T10:32:54.9096158</datestamp><bib-version>v2</bib-version><id>71610</id><entry>2026-03-11</entry><title>Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework</title><swanseaauthors><author><sid>11ddf61c123b99e59b00fa1479367582</sid><ORCID>0000-0003-0371-9646</ORCID><firstname>Cheng</firstname><surname>Cheng</surname><name>Cheng Cheng</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2026-03-11</date><deptcode>MACS</deptcode><abstract>In smart grids, data collection is carried out through smart meters and devices of the Internet of Things, which are installed in the home, allowing to predict the demand for electricity and optimize the distribution of energy. Although the smart grids improve efficiency of operations for end users, they simultaneously present pronounced challenges regarding user privacy and security at the system level. In the context of conventional centralized machine learning, paradigms risk breaching the raw data of consumers, while decentralized paradigms often lack strong mechanisms for verifying identity or ensuring traceability. Existing federated learning systems often lack client level differential privacy, secure aggregation, and decentralized identity protection, leaving them vulnerable to privacy leakage and inference attacks. Blockchain based solutions typically expose model updates or use single layer identifiers. This paper introduces a secure and privacy preserving architecture that combines a dual layer blockchain architecture, federated learning (FL) and central differential privacy (DP) to thoroughly solve these challenges. The proposed system includes a dual layer blockchain system that ensures secure and tamper resistant logging of client interactions and protects client identities by storing salted cryptographic hashes. This design provides both traceability and anonymity, and thus maintains the integrity of participation while obfuscating sensitive identifiers. Privacy is guaranteed by storing raw data in client devices and sending only model updates for central aggregation. At the server side, Gaussian noise is added to the aggregated model parameters to achieve central DP, so as to reduce the risks of inference attacks on user data. Implementation of the proposed framework was performed based on Flower to test the PRECON (Pakistan Residential Electricity CONsumption) dataset, which consists of real-world household electricity consumption data. Multiple machine learning models were benchmarked and out of all the models, Random Forest performed best with the performance metrics of Mean Absolute Error (MAE) of 0.153, Mean Absolute Percentage Error (MAPE) of 0.085 and Mean Squared Error (MSE) of 0.143. The results showed that the proposed framework improved data privacy, preserved the forecasting accuracy and security in smart grid environments.</abstract><type>Journal Article</type><journal>PLOS One</journal><volume>21</volume><journalNumber>3</journalNumber><paginationStart>e0342454</paginationStart><paginationEnd/><publisher>Public Library of Science (PLoS)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1932-6203</issnElectronic><keywords/><publishedDay>2</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-03-02</publishedDate><doi>10.1371/journal.pone.0342454</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders>UKRI EPSRC Grant EP/W020408/1 Project SPRITE+ 2: The Security, Privacy, Identity and Trust Engagement Network plus (phase 2); PhD project RS718 on Explainable AI through UKRI EPSRC Grant funded Doctoral Training Centre at Swansea University; Ongoing Research Funding Program (ORF-2026-947), King Saud University, Riyadh, Saudi Arabia</funders><projectreference/><lastEdited>2026-03-11T10:32:54.9096158</lastEdited><Created>2026-03-11T10:24:07.4564966</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Fatima</firstname><surname>Tariq</surname><orcid>0000-0002-6286-5451</orcid><order>1</order></author><author><firstname>Fatima</firstname><surname>Anjum</surname><order>2</order></author><author><firstname>Cheng</firstname><surname>Cheng</surname><orcid>0000-0003-0371-9646</orcid><order>3</order></author><author><firstname>Shazia</firstname><surname>Javed</surname><orcid>0000-0001-5075-5557</orcid><order>4</order></author><author><firstname>Khursheed</firstname><surname>Aurangzeb</surname><order>5</order></author><author><firstname>Nadia</firstname><surname>Kanwal</surname><order>6</order></author></authors><documents><document><filename>71610__36387__d4e0a3ac18084e8fb3170c613af52381.pdf</filename><originalFilename>71610.VOR.pdf</originalFilename><uploaded>2026-03-11T10:30:58.4130262</uploaded><type>Output</type><contentLength>9063313</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2026 Tariq et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2026-03-11T10:32:54.9096158 v2 71610 2026-03-11 Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2026-03-11 MACS In smart grids, data collection is carried out through smart meters and devices of the Internet of Things, which are installed in the home, allowing to predict the demand for electricity and optimize the distribution of energy. Although the smart grids improve efficiency of operations for end users, they simultaneously present pronounced challenges regarding user privacy and security at the system level. In the context of conventional centralized machine learning, paradigms risk breaching the raw data of consumers, while decentralized paradigms often lack strong mechanisms for verifying identity or ensuring traceability. Existing federated learning systems often lack client level differential privacy, secure aggregation, and decentralized identity protection, leaving them vulnerable to privacy leakage and inference attacks. Blockchain based solutions typically expose model updates or use single layer identifiers. This paper introduces a secure and privacy preserving architecture that combines a dual layer blockchain architecture, federated learning (FL) and central differential privacy (DP) to thoroughly solve these challenges. The proposed system includes a dual layer blockchain system that ensures secure and tamper resistant logging of client interactions and protects client identities by storing salted cryptographic hashes. This design provides both traceability and anonymity, and thus maintains the integrity of participation while obfuscating sensitive identifiers. Privacy is guaranteed by storing raw data in client devices and sending only model updates for central aggregation. At the server side, Gaussian noise is added to the aggregated model parameters to achieve central DP, so as to reduce the risks of inference attacks on user data. Implementation of the proposed framework was performed based on Flower to test the PRECON (Pakistan Residential Electricity CONsumption) dataset, which consists of real-world household electricity consumption data. Multiple machine learning models were benchmarked and out of all the models, Random Forest performed best with the performance metrics of Mean Absolute Error (MAE) of 0.153, Mean Absolute Percentage Error (MAPE) of 0.085 and Mean Squared Error (MSE) of 0.143. The results showed that the proposed framework improved data privacy, preserved the forecasting accuracy and security in smart grid environments. Journal Article PLOS One 21 3 e0342454 Public Library of Science (PLoS) 1932-6203 2 3 2026 2026-03-02 10.1371/journal.pone.0342454 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) UKRI EPSRC Grant EP/W020408/1 Project SPRITE+ 2: The Security, Privacy, Identity and Trust Engagement Network plus (phase 2); PhD project RS718 on Explainable AI through UKRI EPSRC Grant funded Doctoral Training Centre at Swansea University; Ongoing Research Funding Program (ORF-2026-947), King Saud University, Riyadh, Saudi Arabia 2026-03-11T10:32:54.9096158 2026-03-11T10:24:07.4564966 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Fatima Tariq 0000-0002-6286-5451 1 Fatima Anjum 2 Cheng Cheng 0000-0003-0371-9646 3 Shazia Javed 0000-0001-5075-5557 4 Khursheed Aurangzeb 5 Nadia Kanwal 6 71610__36387__d4e0a3ac18084e8fb3170c613af52381.pdf 71610.VOR.pdf 2026-03-11T10:30:58.4130262 Output 9063313 application/pdf Version of Record true © 2026 Tariq et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
spellingShingle Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
Cheng Cheng
title_short Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
title_full Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
title_fullStr Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
title_full_unstemmed Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
title_sort Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Fatima Tariq
Fatima Anjum
Cheng Cheng
Shazia Javed
Khursheed Aurangzeb
Nadia Kanwal
format Journal article
container_title PLOS One
container_volume 21
container_issue 3
container_start_page e0342454
publishDate 2026
institution Swansea University
issn 1932-6203
doi_str_mv 10.1371/journal.pone.0342454
publisher Public Library of Science (PLoS)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description In smart grids, data collection is carried out through smart meters and devices of the Internet of Things, which are installed in the home, allowing to predict the demand for electricity and optimize the distribution of energy. Although the smart grids improve efficiency of operations for end users, they simultaneously present pronounced challenges regarding user privacy and security at the system level. In the context of conventional centralized machine learning, paradigms risk breaching the raw data of consumers, while decentralized paradigms often lack strong mechanisms for verifying identity or ensuring traceability. Existing federated learning systems often lack client level differential privacy, secure aggregation, and decentralized identity protection, leaving them vulnerable to privacy leakage and inference attacks. Blockchain based solutions typically expose model updates or use single layer identifiers. This paper introduces a secure and privacy preserving architecture that combines a dual layer blockchain architecture, federated learning (FL) and central differential privacy (DP) to thoroughly solve these challenges. The proposed system includes a dual layer blockchain system that ensures secure and tamper resistant logging of client interactions and protects client identities by storing salted cryptographic hashes. This design provides both traceability and anonymity, and thus maintains the integrity of participation while obfuscating sensitive identifiers. Privacy is guaranteed by storing raw data in client devices and sending only model updates for central aggregation. At the server side, Gaussian noise is added to the aggregated model parameters to achieve central DP, so as to reduce the risks of inference attacks on user data. Implementation of the proposed framework was performed based on Flower to test the PRECON (Pakistan Residential Electricity CONsumption) dataset, which consists of real-world household electricity consumption data. Multiple machine learning models were benchmarked and out of all the models, Random Forest performed best with the performance metrics of Mean Absolute Error (MAE) of 0.153, Mean Absolute Percentage Error (MAPE) of 0.085 and Mean Squared Error (MSE) of 0.143. The results showed that the proposed framework improved data privacy, preserved the forecasting accuracy and security in smart grid environments.
published_date 2026-03-02T05:25:16Z
_version_ 1859523199419547648
score 11.099629

Similar Items