Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914785375813632 |
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| author | Huo, Wei Chen, Xiaomeng Ding, Kemi Dey, Subhrakanti Shi, Ling |
| author_facet | Huo, Wei Chen, Xiaomeng Ding, Kemi Dey, Subhrakanti Shi, Ling |
| contents | This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression. Our theoretical analysis shows that the algorithm guarantees convergence accuracy, even with aggressive compression errors used to protect privacy. We prove that the algorithm achieves differential privacy through a stochastic quantization scheme. Simulation results for energy consumption games support the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games Huo, Wei Chen, Xiaomeng Ding, Kemi Dey, Subhrakanti Shi, Ling Systems and Control Computer Science and Game Theory This paper explores distributed aggregative games in multi-agent systems. Current methods for finding distributed Nash equilibrium require players to send original messages to their neighbors, leading to communication burden and privacy issues. To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression. Our theoretical analysis shows that the algorithm guarantees convergence accuracy, even with aggressive compression errors used to protect privacy. We prove that the algorithm achieves differential privacy through a stochastic quantization scheme. Simulation results for energy consumption games support the effectiveness of our approach. |
| title | Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games |
| topic | Systems and Control Computer Science and Game Theory |
| url | https://arxiv.org/abs/2405.03106 |