A randomized block policy gradient algorithm with differential privacy in Content Centric Networks
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 i...
Saved in:
Main Authors: | Lin Wang, Xingang Xu, Xuhui Zhao, Baozhu Li, Ruijuan Zheng, Qingtao Wu |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2021-12-01
|
Series: | International Journal of Distributed Sensor Networks |
Online Access: | https://doi.org/10.1177/15501477211059934 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Cache privacy protection strategy in content centric networking
by: Yi ZHU, et al.
Published: (2015-12-01) -
Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework
by: Yihan YU, et al.
Published: (2018-01-01) -
Adaptive selection method of differential privacy GAN gradient clipping thresholds
by: Peng GUO, et al.
Published: (2018-05-01) -
A Temperature Field Based Routing Algorithm in Content-Centric Networking
by: Zonghai Liu, et al.
Published: (2015-12-01) -
Federated Learning Based on Kernel Local Differential Privacy and Low Gradient Sampling
by: Yi Chen, et al.
Published: (2025-01-01)