User Scheduling in Federated Learning over Energy Harvesting Wireless Networks

被引：0

作者：

Hamdi, Rami ^{[1
]}

Chen, Mingzhe ^{[2
]}

Ben Said, Ahmed ^{[3
]}

Qaraqe, Marwa ^{[1
]}

Poor, H. Vincent ^{[2
]}

机构：

[1] Hamad Bin Khalifa Univ, Coll Sci & Engn, Div Informat & Comp Technol, Doha, Qatar

[2] Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544 USA

[3] Qatar Univ, Coll Engn, Comp Sci & Engn Dept, Doha, Qatar

来源：

2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) | 2021年

基金：

美国国家科学基金会;

关键词：

Federated learning; energy harvesting; resource allocation;

D O I：

10.1109/GLOBECOM46510.2021.9685801

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.

引用

页数：6

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