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Exploring Deep Reinforcement Learning- Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT

2022-05-13Journal article Open access
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dc.contributor.authorZheng, Jingjing
dc.contributor.authorLi, Kai
dc.contributor.authorNi, Wei
dc.contributor.authorTovar, Eduardo
dc.contributor.authorGuizani, Mohsen
dc.contributor.authorMhaisen, Naram
dc.date.accessioned2023-01-17T16:31:24Z
dc.date.available2023-01-17T16:31:24Z
dc.date.issued2022-05-13
dc.description.abstractFederated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large datasets for training, which gives rise to a higher energy consumption. On the other hand, the energy consumption can be reduced by selecting the IoT devices with small datasets for FL, resulting in a falling learning accuracy. In this paper, we formulate a new resource allocation problem for privacy-preserving EdgeIoT to balance the learning accuracy of FL and the energy consumption of the IoT device. We propose a new federated learning-enabled twin-delayed deep deterministic policy gradient (FLDLT3) framework to achieve the optimal accuracy and energy balance in a continuous domain. Furthermore, long short term memory (LSTM) is leveraged in FL-DLT3 to predict the time-varying network state while FL-DLT3 is trained to select the IoT devices and allocate the transmit power. Numerical results demonstrate that the proposed FL-DLT3 achieves fast convergence (less than 100 iterations) while the FL accuracy-to-energy consumption ratio is improved by 51.8% compared to existing state-of-the-art benchmark.pt_PT
dc.description.sponsorshipThis work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by national funds through FCT, within project PTDC/EEICOM/3362/2021 (ADANET).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/JIOT.2022.3176739pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/21615
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationUIDP/UIDB/04234/2020pt_PT
dc.relationPTDC/EEICOM/3362/2021pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectFederated learningpt_PT
dc.subjectOnline resource allocationpt_PT
dc.subjectDeep reinforcement learningpt_PT
dc.subjectMobile edge computingpt_PT
dc.subjectInternet of Thingspt_PT
dc.titleExploring Deep Reinforcement Learning- Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoTpt_PT
dc.title.alternative220503pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue21pt_PT
oaire.citation.titleIEEE Internet of Things Journalpt_PT
oaire.citation.volume9pt_PT
person.familyNameLi
person.familyNameTovar
person.givenNameKai
person.givenNameEduardo
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
relation.isAuthorOfPublication.latestForDiscovery21f3fb85-19c2-4c89-afcd-3acb27cedc5e

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