A randomized block policy gradient algorithm with differential privacy in Content Centric Networks

Lin Wang, Xingang Xu, Xuhui Zhao, Baozhu Li, Ruijuan Zheng and Qingtao Wu

International Journal of Distributed Sensor Networks, 2021, vol. 17, issue 12, 15501477211059934

Abstract: Policy gradient methods are effective means to solve the problems of mobile multimedia data transmission in Content Centric Networks. Current policy gradient algorithms impose high computational cost in processing high-dimensional data. Meanwhile, the issue of privacy disclosure has not been taken into account. However, privacy protection is important in data training. Therefore, we propose a randomized block policy gradient algorithm with differential privacy. In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. To solve the privacy protection problem, we add a differential privacy protection mechanism to the algorithm, and we prove that it preserves the ε -privacy level. We conduct extensive simulations in four environments, which are CartPole, Walker, HalfCheetah, and Hopper. Compared with the methods such as important-sampling momentum-based policy gradient, Hessian-Aided momentum-based policy gradient, REINFORCE, the experimental results of our algorithm show a faster convergence rate than others in the same environment.

Keywords: Content Centric Networks; differential privacy protection; randomized block coordinate (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:17:y:2021:i:12:p:15501477211059934

DOI: 10.1177/15501477211059934

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