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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.19453 |
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| _version_ | 1866910892444090368 |
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| author | Ramos, Guilherme Silvestre, Daniel Teixeira, André M. H. Pequito, Sérgio |
| author_facet | Ramos, Guilherme Silvestre, Daniel Teixeira, André M. H. Pequito, Sérgio |
| contents | This paper addresses the challenge of achieving private and resilient average consensus among a group of discrete-time networked agents without compromising accuracy. State-of-the-art solutions to attain privacy and resilient consensus entail an explicit trade-off between the two with an implicit compromise on accuracy. In contrast, in the present work, we propose a methodology that avoids trade-offs between privacy, resilience, and accuracy. We design a methodology that, under certain conditions, enables non-faulty agents, i.e., agents complying with the established protocol, to reach average consensus in the presence of faulty agents, while keeping the non-faulty agents' initial states private. For privacy, agents strategically add noise to obscure their original state, while later withdrawing a function of it to ensure accuracy. Besides, and unlikely many consensus methods, our approach does not require each agent to compute the left-eigenvector of the dynamics matrix associated with the eigenvalue one. Moreover, the proposed framework has a polynomial time complexity relative to the number of agents and the maximum quantity of faulty agents. Finally, we illustrate our method with examples covering diverse faulty agents scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19453 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Average consensus with resilience and privacy guarantees without losing accuracy Ramos, Guilherme Silvestre, Daniel Teixeira, André M. H. Pequito, Sérgio Optimization and Control This paper addresses the challenge of achieving private and resilient average consensus among a group of discrete-time networked agents without compromising accuracy. State-of-the-art solutions to attain privacy and resilient consensus entail an explicit trade-off between the two with an implicit compromise on accuracy. In contrast, in the present work, we propose a methodology that avoids trade-offs between privacy, resilience, and accuracy. We design a methodology that, under certain conditions, enables non-faulty agents, i.e., agents complying with the established protocol, to reach average consensus in the presence of faulty agents, while keeping the non-faulty agents' initial states private. For privacy, agents strategically add noise to obscure their original state, while later withdrawing a function of it to ensure accuracy. Besides, and unlikely many consensus methods, our approach does not require each agent to compute the left-eigenvector of the dynamics matrix associated with the eigenvalue one. Moreover, the proposed framework has a polynomial time complexity relative to the number of agents and the maximum quantity of faulty agents. Finally, we illustrate our method with examples covering diverse faulty agents scenarios. |
| title | Average consensus with resilience and privacy guarantees without losing accuracy |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.19453 |