Low-Latency Federated Reinforcement Learning-Based Resource Allocation in Converged Access Networks

被引：0

作者：

Ruan, Lihua ^{[1
]}

Mondal, Sourav ^{[1
]}

Dias, Imali ^{[1
]}

Wong, Elaine ^{[1
]}

机构：

[1] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia

来源：

2020 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXPOSITION (OFC) | 2020年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose a federated reinforcement learning (FedRL) solution to innovate resource allocation in converged access networks. FedRL lowers network latency with reinforcement-learnt bandwidth decision and achieves fast learning with federated learning efforts.

引用

页数：3