@inproceedings{7a7fb7656c85412e8a5a0c1af1a91200,
title = "Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching",
abstract = "This paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we formu-late the optimization problem with the goal of minimizing long-term content delivery delay. Second, we model the optimization problem as a Partially Observable Markov Decision Process, and propose a Federated Distributed Deep Deterministic Policy Gradient-based method (FD3PG) to solve the corresponding problem. In conclusion, simulation results demonstrate that the proposed FD3PG achieves not only a much lower content delivery delay but also a higher cache hit rate compared with other baselines in various scenarios.",
keywords = "content recommendation, deep reinforcement learning, dis-tributed training, edge caching, federated learning",
author = "Hao Wang and Huan Zhou and Mingze Lit and Liang Zhao and Leung, \{Victor C.M.\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024 ; Conference date: 20-05-2024",
year = "2024",
doi = "10.1109/INFOCOMWKSHPS61880.2024.10620845",
language = "英语",
series = "IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2024",
}