Towards Efficient Edge Caching: A Federated Reinforcement Learning Approach in Cloud-Edge-End Networks

Zhengquan Su; Huiling Shi; Hao Hao; Wei Zhang

doi:10.1109/msn63567.2024.00169

ScienceGate Book Chapters

JOURNAL ARTICLE

Towards Efficient Edge Caching: A Federated Reinforcement Learning Approach in Cloud-Edge-End Networks

Zhengquan Su Huiling Shi Hao Hao Wei Zhang

Year: 2024 Pages: 1190-1191

DOI: 10.1109/msn63567.2024.00169

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Enhanced Data Rates for GSM Evolution Cloud computing Reinforcement learning Edge device Computer network Edge computing End-to-end principle Distributed computing Artificial intelligence Operating system

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.39

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Caching and Content Delivery

Physical Sciences → Computer Science → Computer Networks and Communications

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Towards Efficient Edge Caching: A Federated Reinforcement Learning Approach in Cloud-Edge-End Networks

Metrics

Topics

Related Documents

Federated Deep Reinforcement Learning for Recommendation-Enabled Edge Caching in Mobile Edge-Cloud Computing Networks

Federated Deep Reinforcement Learning for Multimodal Content Caching in Edge-Cloud Networks

Reinforcement Learning for Energy-efficient Edge Caching in Mobile Edge Networks

Deep reinforcement learning for energy-efficient edge caching in mobile edge networks

Communication-Efficient Federated Deep Reinforcement Learning Based Cooperative Edge Caching in Fog Radio Access Networks