Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching

  title={Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching},
  author={Shengheng Liu and Chong Zheng and Yongming Huang and Tony Q. S. Quek},
  journal={IEEE Journal on Selected Areas in Communications},
Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC… 

Figures from this paper


Privacy-Preserving Federated Reinforcement Learning for Popularity-Assisted Edge Caching
An unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the popularities while achieving privacy-preserving goal and results demonstrate the superiority of the proposed scheme in terms of prediction error and hit rate over the baseline methods.
Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning
Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks, and can greatly improve cache performance, effectively protect users’ privacy and significantly reduce communication costs.
User Preference Learning-Based Edge Caching for Fog Radio Access Network
This paper proposes an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method.
Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching
This work proposes a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework that enables base stations to cooperatively learn a shared predictive model, and proves the expectation convergence of FADE.
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities
In this paper, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown, providing a simple, yet practical asynchronous caching approach.
Security in Mobile Edge Caching with Reinforcement Learning
This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures, and proposes security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks.
Game Theoretical Secure Caching Scheme in Multihoming Edge Computing-Enabled Heterogeneous Networks
A novel secure caching scheme in heterogeneous networks for multihoming users, designed to guarantee the integrity of cached contents and preserve the privacy of mobile users, and a Chinese remainder theorem-based privacy preservation protocol is proposed.
Collaborative Online Edge Caching With Bayesian Clustering in Wireless Networks
This paper theoretically characterize the value of dynamic Bayesian clustering for the long-term edge caching scenario with respect to the regret incurred by the noncluster schemes, and numerical results show that the proposed scheme outperforms the caching algorithms without clustering in the uncertain network scenario.
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
Security analysis shows that the proposed blockchain empowered content caching can achieve security and privacy protection andumerical results based on a real dataset from Uber indicate that the DRL-inspired content caching scheme significantly outperforms two benchmark policies.
Online Edge Caching and Wireless Delivery in Fog-Aided Networks With Dynamic Content Popularity
Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit on the long-term NDT of F-RAN system.