Detection of Malicious Agents in Social Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910500431855616 |
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| author | Shumovskaia, Valentina Kayaalp, Mert Sayed, Ali H. |
| author_facet | Shumovskaia, Valentina Kayaalp, Mert Sayed, Ali H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detection of Malicious Agents in Social Learning Shumovskaia, Valentina Kayaalp, Mert Sayed, Ali H. Social and Information Networks Multiagent Systems Signal Processing 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. |
| title | Detection of Malicious Agents in Social Learning |
| topic | Social and Information Networks Multiagent Systems Signal Processing |
| url | https://arxiv.org/abs/2403.12619 |