Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching

2021-10-20 · Shengheng Liu, Chong Zheng, Yongming Huang, Tony Q. S. Quek

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 optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.

📄 PDF Abstract BibTeX arXiv:2110.10349

Code (0)

등록된 구현이 없습니다.

Tasks

Edge-computingFederated LearningPrivacy Preservingreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

2021-10-19 · Xumin Huang, Peichun Li, Rong Yu, Yuan Wu 외

As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking manag…

Deep Reinforcement Learning Edge-computing Federated Learning Management+1

A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale

2025-12-11 · Vinoth Punniyamoorthy, Ashok Gadi Parthi, Mayilsamy Palanigounder, Ravi Kiran Kodali 외 arxiv

Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preser…

Reinforcement Learning Federated Learning

Federated Reinforcement Distillation with Proxy Experience Memory

2019-07-15 · Han Cha, Jihong Park, Hyesung Kim, Seong-Lyun Kim 외

In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, however, contains all the preceding state obse…

Privacy Preserving reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Gradient Inversion in Federated Reinforcement Learning

2025-11-29 · Shenghong He arxiv

Federated reinforcement learning (FRL) enables distributed learning of optimal policies while preserving local data privacy through gradient sharing.However, FRL faces the risk of data privacy leaks, where attackers expl…

Reinforcement Learning Federated Learning Autonomous Driving

Privacy-Preserving Serverless Edge Learning with Decentralized Small Data

2021-11-29 · Shih-Chun Lin, Chia-Hung Lin

In the last decade, data-driven algorithms outperformed traditional optimization-based algorithms in many research areas, such as computer vision, natural language processing, etc. However, extensive data usages bring a …

Cloud Computing Deep Learning Privacy Preserving