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conference paper

Asynchronous Social Learning

Cemri, Mert
•
Bordignon, Virginia  
•
Kayaalp, Mert  
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May 5, 2023
Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
48th IEEE International Conference on Acoustics, Speech and Signal Processing

Social 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.

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Type
conference paper
DOI
10.1109/ICASSP49357.2023.10096238
Scopus ID

2-s2.0-86000375061

Author(s)
Cemri, Mert
•
Bordignon, Virginia  
•
Kayaalp, Mert  
•
Shumovskaia, Valentina  
•
Sayed, Ali H.  
Date Issued

2023-05-05

Publisher

IEEE

Publisher place

Piscataway, NJ

Published in
Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN of the book

9781728163277

Subjects

adaptive social learning

•

asynchronous updates

•

graph learning

•

Social learning

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LOGMIND
ASL  
Event nameEvent acronymEvent placeEvent date
48th IEEE International Conference on Acoustics, Speech and Signal Processing

ICASSP 2023

Rhodes Island, Greece

2023-06-04 - 2023-06-10

FunderFunding(s)Grant NumberGrant URL

SNSF

205121-184999

Available on Infoscience
March 20, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/248107
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