Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input
In this paper, we study the privacy-preserving distributed optimization problem on directed graphs, aiming to minimize the sum of all agents' cost functions and protect the sensitive information. In the distributed optimization problem of directed graphs, agents need to exchange information wit...
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AIMS Press
2025-03-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2025067 |
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| author | Mengjie Xu Nuerken Saireke Jimin Wang |
| author_facet | Mengjie Xu Nuerken Saireke Jimin Wang |
| author_sort | Mengjie Xu |
| collection | DOAJ |
| description | In this paper, we study the privacy-preserving distributed optimization problem on directed graphs, aiming to minimize the sum of all agents' cost functions and protect the sensitive information. In the distributed optimization problem of directed graphs, agents need to exchange information with their neighbors to obtain the optimal solution, and this situation may lead to the leakage of privacy information. By using the state decomposition method, the algorithm ensures that the sensitive information of the agent will not be obtained by attackers. Before each iteration, each agent decomposes their initial state into two sub-states, one sub-state for normal information exchange with other agents, and the other sub-state is only known to itself and invisible to the outside world. Unlike traditional optimization algorithms applied to directed graphs, instead of using the push-sum algorithm, we introduce the external input, which can reduce the number of communications between agents and save communication resources. We prove that in this case, the algorithm can converge to the optimal solution of the distributed optimization problem. Finally, a numerical simulation is conducted to illustrate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-c607a7955ab84c7d9a7e4cdc3a6b5f40 |
| institution | DOAJ |
| issn | 2688-1594 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-c607a7955ab84c7d9a7e4cdc3a6b5f402025-08-20T03:17:09ZengAIMS PressElectronic Research Archive2688-15942025-03-013331429144510.3934/era.2025067Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external inputMengjie Xu0Nuerken Saireke1Jimin Wang2School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaIn this paper, we study the privacy-preserving distributed optimization problem on directed graphs, aiming to minimize the sum of all agents' cost functions and protect the sensitive information. In the distributed optimization problem of directed graphs, agents need to exchange information with their neighbors to obtain the optimal solution, and this situation may lead to the leakage of privacy information. By using the state decomposition method, the algorithm ensures that the sensitive information of the agent will not be obtained by attackers. Before each iteration, each agent decomposes their initial state into two sub-states, one sub-state for normal information exchange with other agents, and the other sub-state is only known to itself and invisible to the outside world. Unlike traditional optimization algorithms applied to directed graphs, instead of using the push-sum algorithm, we introduce the external input, which can reduce the number of communications between agents and save communication resources. We prove that in this case, the algorithm can converge to the optimal solution of the distributed optimization problem. Finally, a numerical simulation is conducted to illustrate the effectiveness of the proposed method.https://www.aimspress.com/article/doi/10.3934/era.2025067distributed optimizationprivacy-preservingstate decompositiondirected graph |
| spellingShingle | Mengjie Xu Nuerken Saireke Jimin Wang Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input Electronic Research Archive distributed optimization privacy-preserving state decomposition directed graph |
| title | Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input |
| title_full | Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input |
| title_fullStr | Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input |
| title_full_unstemmed | Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input |
| title_short | Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input |
| title_sort | privacy preserving distributed optimization algorithm for directed networks via state decomposition and external input |
| topic | distributed optimization privacy-preserving state decomposition directed graph |
| url | https://www.aimspress.com/article/doi/10.3934/era.2025067 |
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