Statistical Privacy-Preserving Distributed Online Aggregative Games via Mirror Descent with Correlated Perturbations
Year: 2026
Type: article
Abstract: Distributed online aggregative games are widely used to model sequential decision-making problems in dynamic networked systems. However, the repeated information exchange required by distributed algorithms may disclose players’ sensitive local data. This paper investigates a privacy-preserving distributed online aggregative game over multi-agent networks. A distributed online mirror descent algorithm with correlated perturbations is developed to protect local private information. Under standard assumptions, an expected dynamic regret bound and a statistical privacy guarantee are established for the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm and reveal the tradeoff between privacy protection and algorithmic performance.
Source: Mathematics
DOI: 10.3390/math14101731
Authors: Meng Yuan, Rui Yu
Institutions: Tongji University, Jiaxing University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7161563698