Asynchronous social learning

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buir.contributor.authorCemri, Mert
dc.citation.epage5en_US
dc.citation.spage1
dc.contributor.authorCemri, Mert
dc.contributor.authorBordignon, V.
dc.contributor.authorKayaalp, M.
dc.contributor.authorShumovskaia, V.
dc.contributor.authorSayed, A. H.
dc.coverage.spatialRhodes Island, Greece
dc.date.accessioned2024-03-11T11:23:51Z
dc.date.available2024-03-11T11:23:51Z
dc.date.issued2023-06-04
dc.departmentDepartment of Electrical and Electronics Engineering
dc.descriptionConference Name: 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
dc.descriptionDate of Conference: 4 -10 June 2023
dc.description.abstractSocial learning algorithms provide a model for the formation and propagation of opinions over social networks. However, most studies focus on the case in which agents share their information synchronously over regular intervals. In this work, we analyze belief convergence and steady-state learning performance for both traditional and adaptive formulations of social learning under asynchronous behavior by the agents, where some of the agents may decide to abstain from sharing any information with the network at some time instants. We also show how to recover the underlying graph topology from observations of the asynchronous network behavior.
dc.identifier.doi10.1109/ICASSP49357.2023.10096238
dc.identifier.issn1520-6149
dc.identifier.urihttps://hdl.handle.net/11693/114506
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://dx.doi.org/10.1109/ICASSP49357.2023.10096238
dc.rightsCC BY-NC-ND
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.titleICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.subjectAdaptive social learning
dc.subjectAsynchronous updates
dc.subjectGraph learning
dc.subjectSocial learning
dc.titleAsynchronous social learning
dc.typeConference Paper

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