Detection of Malicious Agents in Social Learning Article Swipe

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Valentina Shumovskaia , Mert Kayaalp , Ali H. Sayed ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2403.12619

Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although it is also possible to examine the case of heterogeneous models across the graph. One important special case is when heterogeneity is caused by the presence of malicious agents whose goal is to move the agents toward a wrong hypothesis. In this work, we propose an algorithm that allows discovering the true state of every individual agent based on the sequence of their beliefs. In so doing, the methodology is also able to locate malicious behavior.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2403.12619
PDF: https://arxiv.org/pdf/2403.12619
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4393027425

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OpenAlex ID: https://openalex.org/W4393027425

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DOI: https://doi.org/10.48550/arxiv.2403.12619

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Title: Detection of Malicious Agents in Social Learning

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-03-19

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Authors: Valentina Shumovskaia, Mert Kayaalp, Ali H. Sayed

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Landing page: https://arxiv.org/abs/2403.12619

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PDF URL: https://arxiv.org/pdf/2403.12619

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2403.12619

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Concepts: Computer science, Computer security, Artificial intelligence

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Cited by: 0

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Related works (count): 10

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