Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152724
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dc.contributor.authorLim, Bryan Wei Yangen_US
dc.contributor.authorXiong, Zehuien_US
dc.contributor.authorKang, Jiaweien_US
dc.contributor.authorNiyato, Dusiten_US
dc.contributor.authorLeung, Cyrilen_US
dc.contributor.authorMiao, Chunyanen_US
dc.contributor.authorShen, Xueminen_US
dc.date.accessioned2021-12-09T08:13:25Z-
dc.date.available2021-12-09T08:13:25Z-
dc.date.issued2020-
dc.identifier.citationLim, B. W. Y., Xiong, Z., Kang, J., Niyato, D., Leung, C., Miao, C. & Shen, X. (2020). When information freshness meets service latency in federated learning : a task-aware incentive scheme for smart industries. IEEE Transactions On Industrial Informatics, 18(1), 457-466. https://dx.doi.org/10.1109/TII.2020.3046028en_US
dc.identifier.issn1551-3203en_US
dc.identifier.urihttps://hdl.handle.net/10356/152724-
dc.description.abstractFor several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a Federated Learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and Age of Information (AoI), i.e., the time elapsed between data aggregation from the deployed IIoT devices to completion of the FL based training. On one hand, if the data is collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner towards AoI and service latency. Performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme towards satisfying varying preferences of AoI and service latency.en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipEnergy Market Authority (EMA)en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationAISG-GC-2019-003en_US
dc.relationNRF2017EWT-EP003-041en_US
dc.relationNRF2015-NRF-ISF001-2277en_US
dc.relationM4082187 (4080)en_US
dc.relationRG16/20en_US
dc.relation.ispartofIEEE Transactions on Industrial Informaticsen_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TII.2020.3046028.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleWhen information freshness meets service latency in federated learning : a task-aware incentive scheme for smart industriesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchAlibaba-NTU Joint Research Instituteen_US
dc.contributor.researchJoint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)en_US
dc.identifier.doi10.1109/TII.2020.3046028-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85098755141-
dc.identifier.issue1en_US
dc.identifier.volume18en_US
dc.identifier.spage457en_US
dc.identifier.epage466en_US
dc.subject.keywordsFederated Learningen_US
dc.subject.keywordsAge of Informationen_US
dc.description.acknowledgementThis research is supported, in part, by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003), Singapore Energy Market Authority (EMA), Energy Resilience, under Grant NRF2017EWT-EP003-041; and in part by the Singapore NRF2015-NRF-ISF001-2277. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. This research is also supported by WASP/NTU grant M4082187 (4080) and Singapore Ministry of Education (MOE) Tier 1 (RG16/20).en_US
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